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Search Results (1,222)

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Keywords = water resource monitoring and management

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28 pages, 6962 KiB  
Article
Mapping Drought Incidents in the Mediterranean Region with Remote Sensing: A Step Toward Climate Adaptation
by Aikaterini Stamou, Aikaterini Bakousi, Anna Dosiou, Zoi-Eirini Tsifodimou, Eleni Karachaliou, Ioannis Tavantzis and Efstratios Stylianidis
Land 2025, 14(8), 1564; https://doi.org/10.3390/land14081564 - 30 Jul 2025
Viewed by 240
Abstract
The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are [...] Read more.
The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article
(This article belongs to the Special Issue Land and Drought: An Environmental Assessment Through Remote Sensing)
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20 pages, 8154 KiB  
Article
Strategies for Soil Salinity Mapping Using Remote Sensing and Machine Learning in the Yellow River Delta
by Junyong Zhang, Xianghe Ge, Xuehui Hou, Lijing Han, Zhuoran Zhang, Wenjie Feng, Zihan Zhou and Xiubin Luo
Remote Sens. 2025, 17(15), 2619; https://doi.org/10.3390/rs17152619 - 28 Jul 2025
Viewed by 366
Abstract
In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning [...] Read more.
In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning techniques. Utilizing the SCORPAN model framework, we systematically combined diverse remote sensing datasets and innovatively established nine distinct strategies for soil salinity prediction. We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. The results reveal that among the models evaluated across the nine strategies, the SVR model demonstrated the highest accuracy, followed by RF. Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R2) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation. Full article
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20 pages, 7640 KiB  
Article
Land Cover Mapping Using High-Resolution Satellite Imagery and a Comparative Machine Learning Approach to Enhance Regional Water Resource Management
by János Tamás, Angura Louis, Zsolt Zoltán Fehér and Attila Nagy
Remote Sens. 2025, 17(15), 2591; https://doi.org/10.3390/rs17152591 - 25 Jul 2025
Viewed by 263
Abstract
Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), [...] Read more.
Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), Gradient Tree Boosting (GTB), and Naive Bayes (NB) in creating land cover maps for the Tisza-Körös Valley Irrigation System (TIKEVIR) in Hungary. Water bodies, built-up areas, forests, grasslands, and major crops were among the important land cover categories that were classified for the two agricultural seasons (2018 and 2022). RF performed consistently in 2022 and reached its best accuracy in 2018 (OA = 0.87, KC = 0.83, PI = 0.94). While NB’s performance in 2022 remained less consistent, GTB’s performance increased. The findings show that RF works effectively for generating accurate land cover data, providing useful information for regional monitoring, and assisting in water and environmental management decision-making. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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10 pages, 393 KiB  
Proceeding Paper
Artificial Intelligence for Optimal Water Resource Management: A Literature Review
by Wissal Ed-Dehbi, Mustapha Ahlaqqach and Jamal Benhra
Eng. Proc. 2025, 97(1), 52; https://doi.org/10.3390/engproc2025097052 - 24 Jul 2025
Viewed by 288
Abstract
This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and [...] Read more.
This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation. Full article
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22 pages, 2420 KiB  
Article
BiEHFFNet: A Water Body Detection Network for SAR Images Based on Bi-Encoder and Hybrid Feature Fusion
by Bin Han, Xin Huang and Feng Xue
Mathematics 2025, 13(15), 2347; https://doi.org/10.3390/math13152347 - 23 Jul 2025
Viewed by 193
Abstract
Water body detection in synthetic aperture radar (SAR) imagery plays a critical role in applications such as disaster response, water resource management, and environmental monitoring. However, it remains challenging due to complex background interference in SAR images. To address this issue, a bi-encoder [...] Read more.
Water body detection in synthetic aperture radar (SAR) imagery plays a critical role in applications such as disaster response, water resource management, and environmental monitoring. However, it remains challenging due to complex background interference in SAR images. To address this issue, a bi-encoder and hybrid feature fuse network (BiEHFFNet) is proposed for achieving accurate water body detection. First, a bi-encoder structure based on ResNet and Swin Transformer is used to jointly extract local spatial details and global contextual information, enhancing feature representation in complex scenarios. Additionally, the convolutional block attention module (CBAM) is employed to suppress irrelevant information of the output features of each ResNet stage. Second, a cross-attention-based hybrid feature fusion (CABHFF) module is designed to interactively integrate local and global features through cross-attention, followed by channel attention to achieve effective hybrid feature fusion, thus improving the model’s ability to capture water structures. Third, a multi-scale content-aware upsampling (MSCAU) module is designed by integrating atrous spatial pyramid pooling (ASPP) with the Content-Aware ReAssembly of FEatures (CARAFE), aiming to enhance multi-scale contextual learning while alleviating feature distortion caused by upsampling. Finally, a composite loss function combining Dice loss and Active Contour loss is used to provide stronger boundary supervision. Experiments conducted on the ALOS PALSAR dataset demonstrate that the proposed BiEHFFNet outperforms existing methods across multiple evaluation metrics, achieving more accurate water body detection. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Remote Sensing)
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25 pages, 1882 KiB  
Article
An Assessment of Collector-Drainage Water and Groundwater—An Application of CCME WQI Model
by Nilufar Rajabova, Vafabay Sherimbetov, Rehan Sadiq and Alaa Farouk Aboukila
Water 2025, 17(15), 2191; https://doi.org/10.3390/w17152191 - 23 Jul 2025
Viewed by 515
Abstract
According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions [...] Read more.
According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article
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26 pages, 11237 KiB  
Article
Reclassification Scheme for Image Analysis in GRASS GIS Using Gradient Boosting Algorithm: A Case of Djibouti, East Africa
by Polina Lemenkova
J. Imaging 2025, 11(8), 249; https://doi.org/10.3390/jimaging11080249 - 23 Jul 2025
Viewed by 464
Abstract
Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping [...] Read more.
Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article
(This article belongs to the Special Issue Self-Supervised Learning for Image Processing and Analysis)
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22 pages, 2461 KiB  
Article
Environmental Drivers of Phytoplankton Structure in a Semi-Arid Reservoir
by Fangze Zi, Tianjian Song, Wenxia Cai, Jiaxuan Liu, Yanwu Ma, Xuyuan Lin, Xinhong Zhao, Bolin Hu, Daoquan Ren, Yong Song and Shengao Chen
Biology 2025, 14(8), 914; https://doi.org/10.3390/biology14080914 - 22 Jul 2025
Viewed by 303
Abstract
Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental [...] Read more.
Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article
(This article belongs to the Special Issue Wetland Ecosystems (2nd Edition))
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29 pages, 4438 KiB  
Review
Microfluidic Sensors Integrated with Smartphones for Applications in Forensics, Agriculture, and Environmental Monitoring
by Tadsakamon Loima, Jeong-Yeol Yoon and Kattika Kaarj
Micromachines 2025, 16(7), 835; https://doi.org/10.3390/mi16070835 - 21 Jul 2025
Viewed by 558
Abstract
The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated [...] Read more.
The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated microfluidic sensors, focusing on their design, fabrication, smartphone integration, and analytical functions with the applications in forensic science, agriculture, and environmental monitoring. In forensic science, these sensors provide fast, field-based alternatives to traditional lab methods for detecting substances like DNA, drugs, and explosives, improving investigation efficiency. In agriculture, they support precision farming by enabling on-demand analysis of soil nutrients, water quality, and plant health, enhancing crop management. In environmental monitoring, these sensors allow the timely detection of pollutants in air, water, and soil, enabling quicker responses to hazards. Their portability and user-friendliness make them particularly valuable in resource-limited settings. Overall, this review highlights the transformative potential of smartphone-based microfluidic sensors in enabling accessible, real-time diagnostics across multiple disciplines. Full article
(This article belongs to the Special Issue Microfluidic-Based Sensing)
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25 pages, 2201 KiB  
Article
Evolutionary-Assisted Data-Driven Approach for Dissolved Oxygen Modeling: A Case Study in Kosovo
by Bruno da S. Macêdo, Larissa Lima, Douglas Lima Fonseca, Tales H. A. Boratto, Camila M. Saporetti, Osman Fetoshi, Edmond Hajrizi, Pajtim Bytyçi, Uilson R. V. Aires, Roland Yonaba, Priscila Capriles and Leonardo Goliatt
Earth 2025, 6(3), 81; https://doi.org/10.3390/earth6030081 - 21 Jul 2025
Viewed by 289
Abstract
Dissolved oxygen (DO) is widely recognized as a fundamental parameter in assessing water quality, given its critical role in supporting aquatic ecosystems. Accurate estimation of DO levels is crucial for effective management of riverine environments, especially in anthropogenically stressed regions. In this study, [...] Read more.
Dissolved oxygen (DO) is widely recognized as a fundamental parameter in assessing water quality, given its critical role in supporting aquatic ecosystems. Accurate estimation of DO levels is crucial for effective management of riverine environments, especially in anthropogenically stressed regions. In this study, a hybrid machine learning (ML) framework is introduced to predict DO concentrations, where optimization is performed through Genetic Algorithm Search with Cross-Validation (GASearchCV). The methodology was applied to a dataset collected from the Sitnica River in Kosovo, comprising more than 18,000 observations of temperature, conductivity, pH, and dissolved oxygen. The ML models Elastic Net (EN), Support Vector Regression (SVR), and Light Gradient Boosting Machine (LGBM) were fine-tuned using cross-validation and assessed using five performance metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error MARE, and mean square error (MSE). Among them, the LGBM model yielded the best predictive results, achieving an R2 of 0.944 and RMSE of 8.430 mg/L on average. A Monte Carlo Simulation-based uncertainty analysis further confirmed the model’s robustness, enabling comparison of the trade-off between uncertainty and predictive precision. Comparison with recent studies confirms the proposed framework’s competitive performance, demonstrating the effectiveness of automated tuning and ensemble learning in achieving reliable and real-time water quality forecasting. The methodology offers a scalable and reliable solution for advancing data-driven water quality forecasting, with direct applicability to real-time environmental monitoring and sustainable resource management. Full article
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24 pages, 7947 KiB  
Article
Spatial Downscaling of GRACE Groundwater Storage Based on DTW Distance Clustering and an Analysis of Its Driving Factors
by Huazhu Xue, Hao Wang, Guotao Dong and Zhi Li
Remote Sens. 2025, 17(14), 2526; https://doi.org/10.3390/rs17142526 - 20 Jul 2025
Viewed by 383
Abstract
High-resolution groundwater storage is essential for effective regional water resource management. While Gravity Recovery and Climate Experiment (GRACE) satellite data offer global coverage, the coarse spatial resolution (0.25–0.5°) limits the data’s applicability at regional scales. Traditional downscaling methods often fail to effectively capture [...] Read more.
High-resolution groundwater storage is essential for effective regional water resource management. While Gravity Recovery and Climate Experiment (GRACE) satellite data offer global coverage, the coarse spatial resolution (0.25–0.5°) limits the data’s applicability at regional scales. Traditional downscaling methods often fail to effectively capture spatial heterogeneity within regions, leading to reduced model performance. To overcome this limitation, a zoned downscaling strategy based on time series clustering is proposed. A K-means clustering algorithm with dynamic time warping (DTW) distance, combined with a random forest (RF) model, was employed to partition the Hexi Corridor region into relatively homogeneous subregions for downscaling. Results demonstrated that this clustering strategy significantly enhanced downscaling model performance. Correlation coefficients rose from 0.10 without clustering to above 0.84 with K-means clustering and the RF model, while correlation with the groundwater monitoring well data improved from a mean of 0.47 to 0.54 in the first subregion (a) and from 0.40 to 0.45 in the second subregion (b). The driving factor analysis revealed notable differences in dominant factors between subregions. In the first subregion (a), potential evapotranspiration (PET) was found to be the primary driving factor, accounting for 33.70% of the variation. In the second subregion (b), the normalized difference vegetation index (NDVI) was the dominant factor, contributing 29.73% to the observed changes. These findings highlight the effectiveness of spatial clustering downscaling methods based on DTW distance, which can mitigate the effects of spatial heterogeneity and provide high-precision groundwater monitoring data at a 1 km spatial resolution, ultimately improving water resource management in arid regions. Full article
(This article belongs to the Special Issue Remote Sensing for Groundwater Hydrology)
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22 pages, 5937 KiB  
Article
CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution
by Weiliang Liang and Yuan Liu
Remote Sens. 2025, 17(14), 2513; https://doi.org/10.3390/rs17142513 - 19 Jul 2025
Viewed by 413
Abstract
Meteorological satellites play a critical role in weather forecasting, climate monitoring, water resource management, and more. These satellites feature an array of radiative imaging bands, capturing dozens of spectral images that span from visible to infrared. However, the spatial resolution of these bands [...] Read more.
Meteorological satellites play a critical role in weather forecasting, climate monitoring, water resource management, and more. These satellites feature an array of radiative imaging bands, capturing dozens of spectral images that span from visible to infrared. However, the spatial resolution of these bands varies, with images at longer wavelengths typically exhibiting lower spatial resolutions, which limits the accuracy and reliability of subsequent applications. To alleviate this issue, we propose a channel–spatial attention-based network, named CSAN, designed to super-resolve all low-resolution (LR) bands to the available maximal high-resolution (HR) scale. The CSAN consists of an information fusion unit, a feature extraction module, and an image restoration unit. The information fusion unit adaptively fuses LR and HR images, effectively capturing inter-band spectral relationships and spatial details to enhance the input representation. The feature extraction module integrates channel and spatial attention into the residual network, enabling the extraction of informative spectral and spatial features from the fused inputs. Using these deep features, the image restoration unit reconstructs the missing spatial details in LR images. Extensive experiments demonstrate that the proposed network outperforms other state-of-the-art approaches quantitatively and visually. Full article
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14 pages, 137609 KiB  
Article
Monitoring Regional Terrestrial Water Storage Variations Using GNSS Data
by Dejian Wu, Jian Qin and Hao Chen
Water 2025, 17(14), 2128; https://doi.org/10.3390/w17142128 - 17 Jul 2025
Viewed by 323
Abstract
Accurately monitoring terrestrial water storage (TWS) variations is essential due to global climate change and growing water demands. This study investigates TWS changes in Oregon, USA, using Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory, Gravity Recovery and Climate Experiment [...] Read more.
Accurately monitoring terrestrial water storage (TWS) variations is essential due to global climate change and growing water demands. This study investigates TWS changes in Oregon, USA, using Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory, Gravity Recovery and Climate Experiment (GRACE) level-3 mascon data from the Jet Propulsion Laboratory (JPL), and Noah model data from the Global Land Data Assimilation System (GLDAS) data. The results show that the GNSS inversion offers superior spatial resolution, clearly capturing a water storage gradient from 300 mm in the Cascades to 20 mm in the basin and accurately distinguishing between mountainous and basin areas. However, the GRACE data exhibit blurred spatial variability, with the equivalent water height amplitude ranging from approximately 100 mm to 145 mm across the study area, making it difficult to resolve terrestrial water storage gradients. Moreover, GLDAS exhibits limitations in mountainous regions. The GNSS can provide continuous dynamic monitoring, with results aligning well with seasonal trends seen in GRACE and GLDAS data, although with a 1–2 months phase lag compared to the precipitation data, reflecting hydrological complexity. Future work may incorporate geological constraints, region-specific elastic models, and regularization strategies to improve monitoring accuracy. This study demonstrates the strong potential of GNSS technology for monitoring TWS dynamics and supporting environmental assessment, disaster warning, and water resource management. Full article
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18 pages, 5039 KiB  
Article
Global Research Trends on Water Contamination by Microorganisms: A Bibliometric Analysis
by Zoila Isabel Cárdenas Tirado, Isaías Wilmer Duenas Sayaverde, Rosario del Socorro Avellaneda Yajahuanca, Sdenka Caballero Aparicio, Kelly Myriam Jiménez de Aliaga, Edo Gallegos Aparicio, Maria Antonieta Rubio Tyrrel, Maria do Livramento Fortes Figueiredo, José Wicto Pereira Borges, Rosilane de Lima Brito Magalhães, Denise Andrade, Daniela Reis Joaquim de Freitas, Ana Raquel Batista de Carvalho and Maria Eliete Batista Moura
Int. J. Environ. Res. Public Health 2025, 22(7), 1128; https://doi.org/10.3390/ijerph22071128 - 17 Jul 2025
Viewed by 357
Abstract
Water is an essential resource for life; however, the quality of available water on the planet has been compromised due to various factors, including microbiological contamination. Objective: To analyze the global scientific production of microbiological water contamination using bibliometric methods. Method: A search [...] Read more.
Water is an essential resource for life; however, the quality of available water on the planet has been compromised due to various factors, including microbiological contamination. Objective: To analyze the global scientific production of microbiological water contamination using bibliometric methods. Method: A search for scientific articles was conducted using the advanced query function in the Web of Science™ database, specifically in its core collection, on 26 February 2025. Data from 2000 articles were analyzed using the Bibliometrix package in R (version 4.2.1) and the Biblioshiny application (version 2.0). Results: The evaluated articles were published between 1952 and 2025, with a peak in publications in 2022. The journal Water Research stood out as the most relevant, publishing 128 articles. The Egyptian Knowledge Bank was identified as the most productive institution, while China had the highest number of contributing authors. The most cited article received 475 citations. Additionally, KeyWords Plus™ highlighted the focus of the studies on ecological and biotechnological methods for contaminant removal, as well as the presence of waterborne pathogens and their inactivation methods. Conclusions: The results show a growing interest in the development of ecological and biotechnological methods for contaminant removal and pathogen inactivation in water. The integration of artificial intelligence with real-time monitoring systems emerges as a promising strategy for improving water quality management. These findings highlight the relevance of the topic for public health and health education. Full article
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29 pages, 6561 KiB  
Article
Correction of ASCAT, ESA–CCI, and SMAP Soil Moisture Products Using the Multi-Source Long Short-Term Memory (MLSTM)
by Qiuxia Xie, Yonghui Chen, Qiting Chen, Chunmei Wang and Yelin Huang
Remote Sens. 2025, 17(14), 2456; https://doi.org/10.3390/rs17142456 - 16 Jul 2025
Viewed by 415
Abstract
The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly [...] Read more.
The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm3/cm3, 0.019 cm3/cm3, and 0.034 cm3/cm3, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm3/cm3 (ASCAT) and 0.0662 cm3/cm3 (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
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