Egyptian Journal of Remote Sensing and Space Sciences最新文献_第3页

Temporal glacier velocity variations and their controlling factors in the Nathorstbreen glacier system, Svalbard 斯瓦尔巴群岛Nathorstbreen冰川系统冰川速度变化及其控制因素

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-12-03 DOI: 10.1016/j.ejrs.2025.11.004

Supratim Guha , Hyun-Cheol Kim

Temporal variations in glacier velocity are not only essential to understand glacier dynamics but also to predict glacier hazards. Therefore, in the current study, the continuous glacier velocities were estimated from 2014 to 2023 in the Nathorstbreen Glacier System (NGS), Svalbard, where a recent surge event has been observed. Also, the study identified and quantified the factors controlling variations of annual glacier velocity.

Using Landsat 8 OLI images, Cossi-corr (Co-registration of Optically Sensed Images and Correlation), an advanced Fourier-based image-matching tool, was utilized to estimate the velocity of the NGS. A multivariate regression analysis was performed to evaluate the influence of temperature, precipitation, snowfall, and terminus fluctuations on annual velocity changes.

The results indicate that the NGS exhibited the highest and lowest average annual velocities in 2021 and 2018, with magnitudes of 0.86 ± 0.11 m/day and 0.34 ± 0.18 m/day, respectively. The lower velocity in 2018 represents a quiescent phase following the previous surge, whereas the acceleration in 2021 reflects renewed dynamic activity linked to terminus retreat. Overall, glacier velocity declined from 2014 to 2018, increased between 2020 and 2022, and slightly decreased again in 2023. During this period, the glacier terminus experienced alternating annual retreat and advance, resulting in a net retreat of approximately 2.9 km. Terminus fluctuations were identified as important factors influencing annual glacier velocity, showing a lagged response between terminus movement and velocity. Including parameters such as ice thickness and subglacial hydrology in future analyses would further improve understanding of glacier velocity controls.

冰川流速的时间变化不仅是了解冰川动态的必要条件，而且也是预测冰川灾害的必要条件。因此，在本研究中，估计了2014年至2023年斯瓦尔巴群岛Nathorstbreen冰川系统（NGS）的连续冰川速度，在那里观测到最近的涌流事件。此外，研究还确定并量化了控制冰川年速度变化的因素。利用Landsat 8 OLI图像，利用基于傅里叶的先进图像匹配工具cossicorr （Co-registration of optical sensing images and Correlation）来估计NGS的速度。采用多元回归分析，评价了气温、降水、降雪量和终端线波动对年速度变化的影响。结果表明，NGS的年平均速度在2021年和2018年最高和最低，震级分别为0.86±0.11 m/d和0.34±0.18 m/d。2018年的较低速度代表了之前激增之后的静止阶段，而2021年的加速反映了与终端撤退相关的新的动态活动。总体而言，冰川速度在2014年至2018年期间下降，在2020年至2022年期间增加，并在2023年再次略有下降。在此期间，冰川末端经历了每年交替后退和前进的过程，导致净后退约2.9公里。终端波动是影响冰川年速度的重要因素，终端运动与速度之间存在滞后响应。在未来的分析中包括冰层厚度和冰下水文等参数将进一步提高对冰川速度控制的理解。

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引用次数: 0

Iron ore exploration in the Central Eastern Desert of Egypt: Insights from remote Sensing, Geophysical, and geochemical data 埃及中东部沙漠的铁矿勘探：来自遥感、地球物理和地球化学数据的见解

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 10.1016/j.ejrs.2025.11.001

Mahmoud Abd El-Rahman Hegab , Salem Mohamed Salem , Nehal Mohamed Soliman , Kareem Hamed Abd El Wahid , Soha Hassan , Alaa Nayef , Mohamed Anwar Ahmed

The novelty of this study lies in applying an integrated workflow that combines geological mapping, aeromagnetic analysis, remote sensing, and XRF analysis to delineate extensions of known iron ore deposits and identify previously unrecognized occurrences, while simultaneously providing new insights into the tectono-magmatic controls of iron mineralization in the Central Eastern Desert. The findings provide critical data on the spatial organization, mineral characteristics, and geological controls of iron ore in this complex tectonic setting, enabling more efficient exploration plans in the Eastern Desert. Metamorphosed banded iron formations (BIFs) prevail at several localities, e.g., Gabal El Hadid, Umm Nar, Umm Ghamis El Zarqa, El Sibai, El Dabbah, and Wadi Kareem. These BIFs occur within a metavolcano-sedimentary environment, with thicknesses of up to 5 m, in the form of bands and lenses composed of magnetite, hematite, and silica. Magnetic spectral analysis enabled clear discrimination among lithological units, definition of structural controls, and demarcation of alteration zones associated with iron mineralization.

这项研究的新颖之处在于，它将地质测绘、航磁分析、遥感和XRF分析相结合，应用了一个集成的工作流程，以描绘已知铁矿床的延伸，并识别以前未被识别的矿点，同时为中东部沙漠中铁矿化的构造-岩浆控制提供了新的见解。这一发现为研究这一复杂构造环境下的铁矿石空间组织、矿物特征和地质控制提供了关键数据，有助于制定更有效的东部沙漠勘探计划。变质带状铁地层（BIFs）在Gabal El Hadid、Umm Nar、Umm Ghamis El Zarqa、El Sibai、El Dabbah和Wadi Kareem等几个地方普遍存在。这些bif出现在变质火山-沉积环境中，厚度可达5米，以磁铁矿、赤铁矿和二氧化硅组成的带状和透镜状的形式存在。通过磁谱分析，可以清晰地区分岩性单元，明确构造控制，划分与铁矿成矿有关的蚀变带。

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引用次数: 0

Advanced time series forecasting of vegetation health using deep learning models: A remote sensing approach to analyzing climate change impact 使用深度学习模型的植被健康高级时间序列预测：一种分析气候变化影响的遥感方法

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-10-16 DOI: 10.1016/j.ejrs.2025.09.005

Sarhad Baez Hasan , Shahab Wahhab Kareem

The growing consequences of climate change on vegetation ecosystems require advanced predictive tools for environmental monitoring and adaptive management. This research explored a new application of hybrid deep learning models to forecast the Normalized Difference Vegetation Index (NDVI) time series, using Sentinel-2 high-resolution satellite images. Specifically, this research investigated vegetation dynamics in four climatically different regions of Northern Iraq from 2016 to 2024, developing and comparing eight deep learning models, including traditional recurrent networks (Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)) and Convolutional Neural Networks (CNN), resulting in unique hybrid models that combine spatial and temporal feature extraction mechanisms. The study utilized a large dataset of 43,200 images with a spatial resolution of 10 m, employing systematic data preparation that included NDVI processing (NDVI calculations, normalization, and time-series sequence construction) necessary for model training and learning. The model performance was rigorously evaluated, where hybrid models were demonstrated to outperform other models, with BiLSTM-GRU appearing to deliver high accuracy (coefficient of determination scores R² of up to 0.851) and low prediction errors (Mean Squared Error (MSE) as low as 6.04 × 10⁻⁴). In terms of ecological region, model performance was assessed across regions, as well as across different regions, finding general trends in performance, particularly in regions with homogeneous vegetation cover at each time sampling period. The Monte Carlo dropout method offered the opportunity to infer uncertainty, which in turn helped build confidence in predictions. The predictions for the future periods of 2025–2028 show promising seasonal patterns and long-term trends, which are important with respect to climate-adjusted planning.

气候变化对植被生态系统的影响日益严重，需要先进的预测工具来进行环境监测和适应性管理。本研究探索了混合深度学习模型的新应用，利用Sentinel-2高分辨率卫星图像预测归一化植被指数（NDVI）时间序列。具体而言，本研究以2016 - 2024年伊拉克北部4个气候不同地区的植被动态为研究对象，开发并比较了8种深度学习模型，包括传统递归网络（长短期记忆（LSTM）、双向长短期记忆（BiLSTM）和门控递归单元（GRU））和卷积神经网络（CNN），形成了独特的结合时空特征提取机制的混合模型。本研究利用空间分辨率为10 m的43,200幅图像的大型数据集，采用系统的数据准备，包括NDVI处理（NDVI计算、归一化和时间序列序列构建），这是模型训练和学习所必需的。对模型的性能进行了严格的评估，混合模型被证明优于其他模型，BiLSTM-GRU似乎提供了高精度（决定系数R2高达0.851）和低预测误差（均方误差（MSE）低至6.04 × 10−4）。就生态区域而言，对模型的性能进行了跨区域和不同区域的评估，发现了性能的总体趋势，特别是在每个采样期植被覆盖均匀的区域。蒙特卡洛辍学法提供了推断不确定性的机会，这反过来又有助于建立对预测的信心。对2025-2028年未来时期的预测显示出有希望的季节模式和长期趋势，这对气候调整规划很重要。

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引用次数: 0

Climate impact on spatial patterns of Aedes aegypti abundance in Al-Quseer with distribution maps 气候对Al-Quseer地区埃及伊蚊丰度空间格局的影响及分布图

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1016/j.ejrs.2025.09.002

M.H. Rady , Areej A. Al-Khalaf , M.S. Salama , Islam Abou El-Magd , M. Emam , Shaimaa A.A. Moʼmen , Shaimaa M. Farag , M.S. Yones , Abdelwahab Khalil

The invasion of new mosquito disease vectors can alter the abundance of resident mosquito populations, leading to new vector distribution patterns and associated disease risks. A notable example is the re-invasion of the Red Sea region by Aedes aegypti since 2017, facilitated by the area’s hot and humid conditions. In this study, Ae. aegypti larvae were collected from indoors and outdoors habitats and entomological indices were calculated. To assess the influence of climate on spatial distribution, we utilized Landsat-8 satellite-derived maps of Al Quseer (Red Sea Governorate, Egypt), incorporating key climatic and environmental abiotic factors to develop a cartographic model. This model classified areas into different risk levels for Aedes breeding and prevalence. Our results indicate that the primary climatic and environmental factors affecting Ae. aegypti distribution and abundance were temperature, moisture, and vegetation cover—the latter of which indirectly influences microclimates by providing shade and maintaining humidity, thereby affecting mosquito resting sites and survival. The study identified three major risk levels based on breeding suitability: high-risk areas (0.15 km2), moderate-risk areas (0.47 km2), and limited-risk areas (7.24 km2). Of the total study area (4,659 km2), mosquito activity was detected across 655.62 km2, while 4,003.78 km2 remained unaffected. Urban areas within high-risk zones covered 9.11 km2, whereas only 0.25 km2 of urban districts in Al Quseer fell outside the mosquito’s range. Understanding the ecological drivers of Ae. aegypti abundance and predicting its future distribution provides critical insights into vector biology and potential expansion, offering valuable guidance for integrated dengue control strategies.

新的蚊子病媒的入侵可以改变蚊子种群的丰富程度，导致新的病媒分布模式和相关的疾病风险。一个值得注意的例子是，自2017年以来，埃及伊蚊（Aedes aegypti）在红海地区炎热潮湿的条件下再次入侵。在这项研究中，Ae。在室内和室外生境采集埃及伊蚊幼虫，计算昆虫学指数。为了评估气候对空间分布的影响，我们利用Landsat-8卫星衍生的Al Quseer（红海省，埃及）地图，结合关键的气候和环境非生物因子建立了制图模型。该模型将伊蚊孳生和流行的风险等级划分为不同的地区。研究结果表明，主要的气候和环境因素影响了白蛉的生长。埃及伊蚊的分布和数量取决于温度、湿度和植被覆盖——后者通过提供荫凉和保持湿度间接影响小气候，从而影响蚊子的休息地点和生存。该研究根据育种适宜性确定了3个主要风险等级：高风险区（0.15 km2）、中等风险区（0.47 km2）和有限风险区（7.24 km2）。在总研究面积（4659 km2）中，655.62 km2有蚊虫活动，4003.78 km2未受影响。高风险区的城市面积为9.11平方公里，而Al Quseer的城市地区只有0.25平方公里不在蚊子的活动范围内。了解Ae的生态驱动因素。埃及伊蚊的丰度和对其未来分布的预测为了解媒介生物学和潜在的扩展提供了重要的见解，为登革热综合控制战略提供了有价值的指导。

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引用次数: 0

Deep multimodal unmixing of hyperspectral images using Convolutional Block Attention Module (CBAM) and LiDAR features 基于卷积块注意模块（CBAM）和激光雷达特征的高光谱图像深度多模态解混

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-10-21 DOI: 10.1016/j.ejrs.2025.10.002

M Sreejam, L Agilandeeswari

Hyperspectral image unmixing has garnered considerable attention across various application domains, particularly remote sensing applications. However, relying solely on one modality to distinguish objects with similar spectral information presents several shortcomings. Enhanced performance can be achieved by integrating geographical information from Light Detection and Ranging (LiDAR) data into Unmixing. This paper introduces a new unmixing model that combines hyperspectral and LiDAR data. Impressive data representation and feature extraction using deep learning technology have been employed to develop the Multimodal Hyperspectral Unmixing Model using CBAM (Convolutional Block Attention Module) attention (MHUCBAM). The model exemplifies a sophisticated approach to multimodal unmixing, incorporating Spectral Spatial attention alongside the CBAM. Channel Attention improved the model’s capability to analyze complex spatial and spectral relationships. Our model achieves accurate unmixing of complex environments with effective multimodal data representation and deep feature extraction. Two real-world multimodal unmixing datasets, namely, Houston and Muffle, are used for the performance evaluation. A rigorous ablation analysis was performed to validate the performance of the proposed model. The comparative study with existing unmixing models demonstrated that utilizing latent features from LiDAR data resulted in better unmixing outcomes in terms of both Root Mean Square Error (RMSE) and Spectral Angular Distance (SAD).

高光谱图像解混已经在各个应用领域引起了相当大的关注，特别是遥感应用。然而，仅仅依靠一种模态来区分具有相似光谱信息的物体存在一些缺点。通过将来自光探测和测距（LiDAR）数据的地理信息集成到Unmixing中，可以提高性能。本文介绍了一种结合高光谱和激光雷达数据的新解混模型。使用深度学习技术的令人印象深刻的数据表示和特征提取被用于开发使用CBAM（卷积块注意模块）注意（MHUCBAM）的多模态高光谱解混模型。该模型体现了一种复杂的多模态解混方法，将频谱空间关注与CBAM结合在一起。通道注意提高了模型分析复杂空间和光谱关系的能力。该模型通过有效的多模态数据表示和深度特征提取实现了复杂环境的精确解混。两个真实世界的多模态解混数据集，即Houston和Muffle，用于性能评估。进行了严格的烧蚀分析，以验证所提出模型的性能。与现有解混模型的对比研究表明，利用LiDAR数据的潜在特征在均方根误差（RMSE）和光谱角距离（SAD）方面都可以获得更好的解混结果。

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引用次数: 0

UAV-based agricultural spraying: A study on spiral movements and pesticide optimization 基于无人机的农业喷洒：螺旋运动与农药优化研究

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.ejrs.2025.09.001

Mevlüt İnan , Ali Karci

Unmanned aerial vehicles (UAVs) have become an essential component of precision agriculture, providing enhanced accuracy and operational efficiency in pesticide application. This study presents an innovative spraying protocol that integrates spiral flight trajectories with volumetric classification of olive trees, enhancing operational performance while reducing environmental impact. Using high-resolution UAV imagery in conjunction with advanced image processing, trees were categorized into small, medium, and large canopy-volume classes. For each group, optimized spiral patterns with predefined turn counts and flight altitudes were assigned to achieve uniform droplet deposition across complex canopy structures. Field experiments conducted in the Hekimhan district of Malatya, Türkiye, demonstrated an 85 % improvement in spraying efficiency, a 15 % reduction in chemical usage, and a 20 % decrease in operational time compared with conventional methods. The proposed approach significantly improved targeting precision and minimized off-target drift. These results clearly indicate that the proposed protocol is scalable, environmentally sustainable, and operationally efficient for pesticide application in orchards and other tree-based agricultural systems.This approach demonstrates considerable potential for widespread adoption in precision agriculture, offering a replicable and adaptable framework for enhancing the efficiency and sustainability of pesticide application in diverse orchard systems.

无人机（uav）已成为精准农业的重要组成部分，为农药施用提供了更高的准确性和操作效率。本研究提出了一种创新的喷雾方案，将螺旋飞行轨迹与橄榄树的体积分类相结合，提高了操作性能，同时减少了对环境的影响。利用高分辨率无人机图像结合先进的图像处理，将树木分为小、中、大树冠体积类。对于每一组，优化的螺旋模式与预定义的转弯数和飞行高度分配，以实现均匀的液滴沉积在复杂的冠层结构。在吉尔吉斯斯坦共和国马拉提亚的Hekimhan地区进行的实地试验表明，与传统方法相比，喷洒效率提高了85%，化学品使用量减少了15%，作业时间减少了20%。该方法显著提高了瞄准精度，减小了偏离目标漂移。这些结果清楚地表明，所提出的协议具有可扩展性、环境可持续性和操作效率，适用于果园和其他基于树木的农业系统的农药施用。这种方法在精准农业中具有广泛应用的巨大潜力，为提高不同果园系统中农药施用的效率和可持续性提供了可复制和适应性的框架。

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引用次数: 0

GPS and LiDAR optimizing transformation parameters for localization in autonomous vehicles GPS和LiDAR优化自动驾驶汽车定位变换参数

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1016/j.ejrs.2025.09.004

Sundoss ALMahadeen

Accurate localization is necessary for autonomous vehicles, with the demand for the correct fusion of Global Navigation Satellite System (GNSS) and Light Detection and Ranging (LiDAR) data. Existing static transformation parameter optimization methods do not work well to address dynamic environmental conditions such as GNSS signal weakening in urban canyons and LiDAR inconsistencies in open or obstructed environments. This work presents an LSTM-based technique of real-time transformation parameter optimization, automatically adjusting translation, rotation, and scale factors. The LSTM network processes sequential GNSS and LiDAR data, leveraging temporal correlations to enhance accuracy. Exhaustive experiments on real and simulated data demonstrate that the presented model reduces localization error by 25% compared to traditional techniques. The architecture provides an improvement of robustness over flexibility in complex situations like urban, rural, and tunneling conditions, and hence it is a strong solution for autonomous vehicle navigation

准确的定位对自动驾驶汽车来说是必要的，需要正确融合全球导航卫星系统（GNSS）和光探测和测距（LiDAR）数据。现有的静态变换参数优化方法不能很好地解决城市峡谷中GNSS信号减弱、开放或受阻环境中LiDAR不一致等动态环境条件。本文提出了一种基于lstm的实时变换参数优化技术，自动调整平移、旋转和比例因子。LSTM网络处理连续的GNSS和LiDAR数据，利用时间相关性来提高准确性。在真实数据和仿真数据上的详尽实验表明，该模型与传统定位方法相比，定位误差降低了25%。该架构在城市、农村和隧道条件等复杂情况下提供了鲁棒性和灵活性的改进，因此它是自动车辆导航的强大解决方案

引用次数: 0

Efficient monitoring of groundwater level changes using compressive remote sensing 压缩遥感对地下水位变化的有效监测

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-10-17 DOI: 10.1016/j.ejrs.2025.10.003

Cihan Bayındır , Ali Rıza Alan

In this paper, we propose and discuss the applicability of compressive sensing (CS) for the remote measurement and analysis of groundwater level changes. For this purpose, we consider three watersheds in Turkey and utilize the data acquired by the Gravity Recovery and Climate Experiment (GRACE) satellite at these watersheds. These watersheds are Fırat (Euphrates), Kızılırmak, and Büyük Menderes (Greater Menderes). The data collected by the GRACE satellite have a temporal resolution on the order of months, however, due to operation and maintenance considerations it is known that some of the GRACE data may be missing. Using the time series data collected between 2002 and 2019 at these three watersheds we show that the time series of the groundwater table (GWT) can be reconstructed using CS which utilizes fewer samples than the classical Shannon’s theorem states. Thus, when the CS technique is utilized, measurement times and hardware storage requirements of groundwater sensing systems can be significantly reduced where some errors can be observed in the reconstruction of the GWT level. In some cases, such parameters can be exactly reconstructed by CS even in the presence of missing data if certain sparsity and sampling conditions are satisfied. The CS-based GWT reconstruction technique proposed in this paper can also be extended to measure and analyze other types of data such as in situ groundwater levels, groundwater velocities, and groundwater volume flux data in hydrology and hydraulics.

本文提出并讨论了压缩感知（CS）技术在地下水位变化遥感测量与分析中的适用性。为此，我们考虑了土耳其的三个流域，并利用了重力恢复和气候实验（GRACE）卫星在这些流域获得的数据。这些流域分别是Fırat（幼发拉底河）、Kızılırmak和b y k Menderes（大Menderes）。GRACE卫星收集的数据具有月级的时间分辨率，但是，由于操作和维护方面的考虑，已知一些GRACE数据可能会丢失。利用2002年至2019年在这三个流域收集的时间序列数据，我们表明使用CS可以重建地下水位（GWT）的时间序列，该方法比经典香农定理使用的样本更少。因此，当使用CS技术时，地下水传感系统的测量次数和硬件存储要求可以大大减少，但在重建GWT水位时可以观察到一些误差。在某些情况下，如果满足一定的稀疏性和采样条件，即使存在缺失数据，CS也可以精确地重建这些参数。本文提出的基于cs的GWT重建技术还可以扩展到其他类型的数据，如水文水力学中的地下水位、地下流速、地下水体积通量等数据的测量和分析。

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引用次数: 0

Assessing groundwater storage variations in the Volta River Basin combining remote sensing tools and machine learning downscaling techniques 结合遥感工具和机器学习缩尺技术评估伏特河流域地下水储量的变化

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-09-01 Epub Date: 2025-06-17 DOI: 10.1016/j.ejrs.2025.06.001

Randal Djima Djessou , Xiaoyun Wan , Richard Fiifi Annan , Abdoul-Aziz Bio Sidi D. Bouko

Water resources, vital for sustaining life and driving socio-economic development globally, face increasing pressure, necessitating accurate monitoring of storage variations. In this study, the water storage changes and its main drivers within the VRB are deeply investigated using remote sensing tools. The Gravity Recovery and Climate Experiment (GRACE) satellite derived terrestrial water storage anomalies (TWSA) is the only tool which vertically integrates all hydrological variables, and is suitable for groundwater storage anomalies (GWSA) changes investigation. The present investigation initially uses the Generalized Three-Corned Hat approach followed by a weighted average to merge four GRACE derived TWSA. Three machine learning techniques including XGBoost, LightGBM and Random Forest are applied to downscale TWSA at a spatial resolution of 0.1°. Results showed that (i) the merged TWSA depicts the lowest uncertainty with a median of 0.94 cm. (ii) The LightGBM model yielded the highest R² (0.99) and the lowest rmse (0.69 cm) in test phase. (iii) The LightGBM downscaled product indicated that GWSA increased (0.32 cm/month) over 2002–2022. (iv) The influence of precipitation and evapotranspiration on GWSA appeared to be rather harmless, while the spatial distribution of GWSA and subsurface runoff showed significant positive trend over the pixels connected with dams, reservoirs, and irrigated areas. This suggests that anthropogenic variable is the main driver of GWSA changes within the VRB. (v) Statistically significant positive trends are observed in downscaled GWSA time series and in-situ GWSA measurements.

对维持生命和推动全球社会经济发展至关重要的水资源面临越来越大的压力，因此有必要对储存变化进行准确监测。本研究利用遥感工具，深入研究了VRB内储水量变化及其主要驱动因素。重力恢复与气候实验（GRACE）卫星衍生的陆地蓄水异常（TWSA）是唯一垂直整合所有水文变量的工具，适用于地下水蓄水异常（GWSA）变化调查。本研究最初使用广义三角帽方法，然后加权平均合并四个GRACE衍生的TWSA。采用XGBoost、LightGBM和Random Forest三种机器学习技术对0.1°空间分辨率的TWSA进行了缩小。结果表明：(1)合并后的TWSA具有最低的不确定度，中值为0.94 cm。（ii） LightGBM模型在试验阶段R2最高（0.99），rmse最低（0.69 cm）。（iii） LightGBM缩小产品表明，2002-2022年GWSA增加了0.32 cm/月。（四）降水和蒸散发对GWSA的影响不大，而与坝、库、灌区相连的像元上，GWSA和地下径流的空间分布呈显著的正趋势。这表明，人为变量是VRB内GWSA变化的主要驱动因素。(v)在缩小的GWSA时间序列和现场GWSA测量中观察到统计上显著的正趋势。

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引用次数: 0

Quantitative assessment of spatiotemporal variability in air quality within the Amman-Zarqa urban Area, Jordan 约旦安曼-扎尔卡市区空气质量时空变异的定量评估

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-09-01 Epub Date: 2025-06-26 DOI: 10.1016/j.ejrs.2025.06.002

Abdulla Al-Rawabdeh , Farah Alzu’bi , Ali Almagbile

Many factors influence the concentration of air pollutants, particularly Carbon Monoxide (CO) and Nitrogen Dioxide (NO₂). This research aims to study the spatiotemporal variability of CO and NO₂ on a monthly basis in 2021 and to investigate the relationship between these gases and both natural and anthropogenic factors across seven districts of the Amman-Zarqa urban environment of Jordan. To understand these relationships using regression analysis and the mean relative difference, the CO and NO₂ data extracted from The TROPOspheric Monitoring Instrument (TROPOMI) which is the satellite instrument on board the Copernicus Sentinel-5 Precursor satellite. The results of the mean relative difference indicated that the spatial concentration of CO in the Zarqa districts is higher than in the Amman districts due to industrial activities and low vegetation cover. In contrast, NO₂ is primarily concentrated in the Marka and Qasaba Amman districts than the other districts, which have the highest traffic and population density in the study area. Regression analysis reveals that while the concentration of CO is positively correlated with Land Surface Temperature (LST), Wind Speed (WS), and Wind Direction (WD), with r² values of approximately 0.62, 0.53, and 0.48 respectively. Conversely, a negative relationship is observed with digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and Relative Humidity (RH). For NO₂, a weak positive correlation with the Built-Up (BU) index and Normalized Difference Built-Up Index (NDBI) has been noticed, along with a modest negative correlation with LST, DEM, WS, RH, WD, and NDVI.

许多因素影响空气污染物的浓度，特别是一氧化碳（CO）和二氧化氮（NO2）。本研究旨在研究2021年约旦安曼-扎尔卡城市环境7个区CO和NO2的逐月时空变化，并探讨这些气体与自然和人为因素之间的关系。利用哥白尼哨兵-5前驱卫星上的对流层监测仪器（TROPOMI）采集的CO和NO2数据，利用回归分析和平均相对差来理解这些关系。平均相对差异结果表明，由于工业活动和低植被覆盖，Zarqa地区CO的空间浓度高于Amman地区。NO2主要集中在Marka区和Qasaba Amman区，是研究区交通和人口密度最高的两个区。回归分析表明，CO浓度与地表温度（LST）、风速（WS）和风向（WD）呈正相关，r2分别约为0.62、0.53和0.48。与数字高程模型（DEM）、归一化植被指数（NDVI）和相对湿度（RH）呈负相关。NO2与建成度指数（BU）和归一化差异建成度指数（NDBI）呈弱正相关，与LST、DEM、WS、RH、WD和NDVI呈适度负相关。

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