Hesham El-Askary

Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr⁻¹, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven structural response. The observed differences are primarily attributed to differences in spatial resolution and methodology between point-based FEM outputs and pixel-averaged satellite observations. The study demonstrates that combining satellite-based monitoring with numerical simulations provides a robust and cost-effective framework for dam safety assessment. This integrated approach supports improved interpretation of deformation behavior and offers practical value in extreme conditions, such as during flood events or climate-driven hydrological changes. Furthermore, continued advances in remote sensing and numerical modeling are expected to enhance the reliability of such approaches, making this methodology a transferable and sustainable solution for dam management worldwide.

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km ² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R ² = 0.82), rice (R ² = 0.71), and walnut (R ² = 0.68), with weaker associations for alfalfa (R ² = 0.38) and plums (R ² = 0.26) over the average of growing season. Temporal variations highlighted the high nitrogen efficiency of sunflower, rice, and walnut, demonstrating the capability of hyperspectral sensors to monitor nutrient-water dynamics effectively. These findings emphasize the potential of integrating hyperspectral and thermal data to enhance fertilizer and irrigation strategies. Furthermore, the study aligns with NASAs Surface Biology and Geology (SBG) mission, offering valuable insights into precision agriculture and sustainable crop management under changing climatic conditions. Graphical Abstract Based on the graphical snapshot, this study was conducted to investigate canopy nitrogen (N) and evapotranspiration (ET) dynamics using hyperspectral and thermal remote sensing in a 1,135 km ² agricultural region in Californias Northern Central Valley. This work captures the complex relationships between nutrient-water dynamics and agricultural productivity across five crops: sunflower, rice, walnut, alfalfa, and plums, mapped using USDA NASS Cropland Data Layers. Data collection utilized hyperspectral data from the EMIT satellite and thermal data from Landsat-based OpenET, focusing on spatial and temporal variations across four dates (June 11, June 26, August 3, 2023, and January 11, 2024). Analytical methods included validation of canopy N estimation through vegetation indices (NDVI, NDNI, MCARI) derived from EMIT data, supported by scatter plots, boxplots, and statistical analyses such as ANOVA, alongside crop-specific analysis of the N-ET relationship. The Partial Least Squares Regression (PLSR) model was applied to EMIT hyperspectral data to generate temporal N maps, revealing underlying nutrient patterns. Results demonstrated strong N-ET spatial associations over the average of growing season for sunflower (R ² = 0.82), rice (R ² = 0.71), and walnut (R ² = 0.68), with weaker associations for alfalfa (R ² = 0.38) and plums (R ² = 0.26), while vegetation indices showed high predictive accuracy (R ² up to 0.95 for rice and sunflower). Temporal N, ET, and nitrogen-to-ET ratio (NER) maps highlighted efficient N use in low ET areas during January. The findings underscore the utility of integrating hyperspectral and thermal remote sensing to track nutrient-water dynamics, offering critical insights for developing crop-specific, seasonally adjusted management strategies to optimize resource use in precision agriculture.

Hyperspectral image (HSI) classification plays a vital role in remote sensing by leveraging rich spectral and spatial information for accurate material recognition. However, existing methods, particularly Transformer-based approaches, still face challenges in effectively modeling multiscale spatial–spectral features, preserving local details, and maintaining robustness to noise. To mitigate these limitations, we propose TMCANet, a spectral–spatial Transformer with multiscale convolutional attention, designed to effectively leverage both local and global contextual dependencies for HSI classification. Our design is guided by three core strategies: first, a convolutional feature extraction module, consisting of four convolutional layers, to learn hierarchical spectral multiscale representations and enhance local feature capture; second, a hybrid spatial–spectral cross-fusion attention block, combining local spatial attention, and spectral tokenization attention, to dynamically integrate spatial textures and spectral dependencies, and third a spectral–spatial tokenization transformer equipped with an adaptive cross-layer fusion mechanism to aggregate multilevel features, reduce redundancy, and strengthen global contextual modeling. Furthermore, an enhanced focal loss is adopted to alleviate class imbalance and improve multiclass classification robustness. We validate TMCANet on three benchmark datasets—Indian Pines, Pavia University, and Houston—achieving overall accuracies of 94.91%, 95.49%, and 89.89%, respectively, with performance comparable to or exceeding state-of-the-art baselines. These results demonstrate that the proposed multiscale convolutional attention and hybrid spatial–spectral fusion strategies effectively preserve both local detail and global context, thereby enhancing classification performance and supporting practical applicability in remote sensing.

Urban heat islands (UHIs) pose critical challenges to public health, energy demand, and environmental sustainability, particularly in rapidly expanding urban regions. This study examines the complex relationship between building configurations and integrated green spaces, as well as their combined impact on thermal regulation. It focuses on addressing data quality issues commonly encountered in remote sensing applications. Using highresolution multispectral and thermal imagery, we developed an integrated modeling approach that captures the collective influence of built form and green infrastructure on urban microclimates. A key finding is the significant linear inverse relationship between green space coverage and land surface temperature, underscoring the cooling potential of strategically planned green zones. However, achieving robust insights required addressing several data quality challenges, including image misalignment, atmospheric distortions, variable spatial resolutions, and annotation inconsistencies, which can compromise model performance. We applied preprocessing methods, including geometric correction, atmospheric calibration, and multi-source data fusion, alongside rigorous ground-truthing using in situ temperature measurements. We also implemented data augmentation and label refinement strategies to enhance the training of deep learning models for thermal pattern prediction. This study demonstrates that, when properly corrected, urban microclimate models can accurately capture temperature variations of up to 3.5°C across similar urban settings, providing actionable insights for thermal comfort planning. This study highlights the critical role of high-quality, preprocessed remote sensing data in enabling reliable analysis and offers a framework for integrating urban design and green infrastructure to mitigate UHI effects. These findings have implications for scalable urban cooling strategies and climate-resilient city planning.

Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain, often overlooking the structural characteristics of pixels and spectral bands, such as spatial continuity and spectral dependencies. In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI BS. The heterogeneous graph convolutional network (HGCN) and enhanced self-representation (ESR) serve as the two essential components of the proposed ESR-HGCN. To explore spatial features and the potential hidden interactions among spectral bands, we use the HGCN as the backbone network for heterogeneous graph-based HSI BS. Dual graphs at the pixel and band levels are separately constructed and integrated into the ESR module, where a sparsity constraint is enforced and the original Frobenius norm is replaced with $\ell _{1}$- and $\ell _{2,1}$-norm regularizations to achieve robust BS. Meanwhile, dual graph convolution operations are performed to separately extract spatial and spectral features, thereby seamlessly integrating spectral, spatial, and geometric information, offering significant advantages for HSI BS. Finally, an effective optimization scheme is developed to refine the proposed approach. Experimental findings on representative HSI datasets highlight the superiority of ESR-HGCN over state-of-the-art methods.

The Western U.S. is undergoing notable transformations in its hydrological patterns, distinguished by rising variability and recurrent “whiplash” shifts between extreme wet and dry phases. Our comprehensive analysis of 469 streamflow stations from 1981 to 2023 reveals a substantial increase in hydrological whiplash events, with a peak of 206 stations experiencing dry-to-wet whiplash in the early 1990s. We establish strong links between these streamflow extremes and sub-seasonal to seasonal teleconnection factors, particularly the Western Pacific Oscillation (WP) and Eastern Pacific/North Pacific Oscillation (EPO). Additionally, we demonstrate the combined impacts of the El Niño-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), with MJO Phases 2/3 showing a positive relationship (correlation coefficients = 0.45) with wet conditions in the Pacific Southwest from October to December. Future projections using CMIP5 and CMIP6 models indicate increasing precipitation variability across the region. Notably, the CMIP5 RCP 8.5 scenario projects more volatile conditions than other scenarios. The results emphasize the pressing need for water management strategies that are both adaptive and flexible, to cope with the growing unpredictability of the hydrological cycle in the Western U.S.