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

TriM-Net: Trinityformer-Mamba fusion for road extraction in remote sensing trimnet：用于遥感道路提取的Trinityformer-Mamba融合

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-08-05 DOI: 10.1016/j.ejrs.2025.07.006

Zhenzhong Huang , Hongjuan Shao , Chao Ren , Hongman Li , Haoming Bai , Zhou Lei , Gu Yao , Qinyi Chen

Precise road information extraction is crucial for transportation and intelligent sensing. Recently, the fusion of CNN and Transformer architectures in remote sensing-based road extraction, along with U-shaped semantic segmentation networks, has gained significant attention. However, existing methods rely heavily on global features while overlooking local details, limiting accuracy in complex road scenarios. To address this, we propose Trinityformer-Mamba Network (TriM-Net) to enhance local feature extraction. TriM-Net adopts Trinityformer, a modified Transformer architecture. This architecture optimizes local feature perception and reduces computational overhead by replacing the traditional softmax with an improved self-attention mechanism and a novel normalization method. The feedforward network employs a Kolmogorov-Arnold network (KAN), reducing neuron count while enhancing local detail capture using edge activation functions and the Arnold transform. Additionally, the normalization layer integrates the benefits of BatchNorm and LayerNorm for better performance. Furthermore, TriM-Net incorporates an MT_block built with stacked Mamba networks. By leveraging their internal CausalConv1D and SSM modules, this block enhances modeling and local perception while effectively merging Transformer and CNN information for improved image reconstruction. Experimental results demonstrate TriM-Net’s significant superiority over existing state-of-the-art models. On the LSRV dataset, it outperformed the second-best model with advantages of 2.17% in Precision, 0.34% in Recall, 1.72% in IoU, and 2.09% in F1-score. Similarly, on the Massachusetts Road Dataset, it achieved superior Recall (0.45%), IoU (1.41%), and F1-score (1.07%) over its closest competitor. These substantial improvements highlight TriM-Net’s outstanding performance in road information extraction.

精确的道路信息提取对交通运输和智能传感至关重要。近年来，CNN和Transformer架构的融合以及u型语义分割网络在基于遥感的道路提取中得到了广泛关注。然而，现有的方法严重依赖全局特征，而忽略了局部细节，限制了复杂道路场景的准确性。为了解决这个问题，我们提出了Trinityformer-Mamba Network （TriM-Net）来增强局部特征提取。TriM-Net采用了一种改进的Transformer架构Trinityformer。该体系结构通过改进的自关注机制和新的归一化方法取代传统的softmax，优化了局部特征感知，减少了计算开销。前馈网络采用了Kolmogorov-Arnold网络（KAN），减少了神经元数量，同时利用边缘激活函数和Arnold变换增强了局部细节捕获。此外，规范化层集成了BatchNorm和LayerNorm的优点，以获得更好的性能。此外，TriM-Net还集成了一个MT_block，该MT_block由堆叠的Mamba网络构建。通过利用其内部的CausalConv1D和SSM模块，该块增强了建模和局部感知，同时有效地合并Transformer和CNN信息，以改进图像重建。实验结果表明，TriM-Net比现有的最先进模型具有显著的优势。在LSRV数据集上，它以2.17%的精度、0.34%的召回率、1.72%的IoU和2.09%的F1-score优势优于次优模型。同样，在马萨诸塞州道路数据集上，它比最接近的竞争对手取得了更高的召回率（0.45%）、IoU（1.41%）和f1分数（1.07%）。这些实质性的改进凸显了TriM-Net在道路信息提取方面的卓越性能。

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引用次数: 0

Evaluating the factors affecting landslides using machine learning algorithms (case study: the catchment area of Karun-3 Dam, Iran) 利用机器学习算法评估影响滑坡的因素（案例研究：伊朗Karun-3大坝集水区）

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-07-31 DOI: 10.1016/j.ejrs.2025.07.005

Rahman Zandi , Ghasem Shah Pari Far

Landslides are among the phenomena associated with environmental impacts and human and financial losses worldwide. Investigating environmental issues such as landslides and preparing hazard maps are essential for managers and planners. This study examines and models landslides in the catchment area of Karun-3 Dam located in Khuzestan province, Iran, using six machine learning algorithms, including Random Forest (RF), Boosted Regression Tree (BRT), Generalized Aggregate Model (GAM), Support Vector Model (SVM), Classification and Regression Tree (CART), and Generalized Linear Model (GLM). Thirteen independent parameters were identified as the main parameters. Then, their correlation and effects were examined using 284 old landslides, and machine learning models were validated using efficiency, sensitivity, and accuracy indicators. The validation results showed that although all the models used have sufficient accuracy, the RF model (AUC = 0.982, Efficiency = 0.943) has more accuracy than the other five models. Also, the impact of different factors on landslide generation in various models is not the same. In general, the significance of the mentioned parameters is in the range of 0.043 and 0.160. Comparing the results of different models using a non-parametric test shows more similarities between the models used. In general, the results of various models show that the risk of landslides is generally higher on the steep banks of rivers, in the vicinity of lakes, dams, and roads, and especially in lands with soft lithology such as marl. This fact shows us the influence of anthropogenic factors and natural factors simultaneously.

山体滑坡是全球范围内与环境影响、人员和经济损失相关的现象之一。对管理人员和规划人员来说，调查诸如滑坡之类的环境问题和编制灾害地图是必不可少的。本研究利用随机森林（RF）、增强回归树（BRT）、广义聚合模型（GAM）、支持向量模型（SVM）、分类与回归树（CART）和广义线性模型（GLM）等六种机器学习算法，对伊朗胡齐斯坦省Karun-3大坝集水区的滑坡进行了研究和建模。确定了13个独立参数作为主要参数。然后，使用284个老滑坡来检验它们的相关性和影响，并使用效率、灵敏度和准确性指标验证机器学习模型。验证结果表明，虽然所使用的所有模型都具有足够的准确性，但RF模型（AUC = 0.982, Efficiency = 0.943）的准确性高于其他5种模型。不同因素对不同模型滑坡生成的影响也不尽相同。总的来说，上述参数的显著性在0.043 ~ 0.160之间。使用非参数检验比较不同模型的结果显示所使用的模型之间有更多的相似性。总的来说，各种模型的结果表明，在陡峭的河岸、湖泊、水坝和道路附近，特别是在泥沼等岩性较软的土地上，发生山体滑坡的风险通常较高。这一事实同时向我们表明了人为因素和自然因素的影响。

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引用次数: 0

Advancements and applications of space borne of remote sensing in climate change research: A scoping review 空间遥感在气候变化研究中的进展与应用综述

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-07-23 DOI: 10.1016/j.ejrs.2025.07.004

Ricky Anak Kemarau , Zaini Sakawi , Khairul Nizam Abdul Maulud , Wan Shafrina Wan Mohd Jaafar , Stanley Anak Suab , Oliver Valentine Eboy , Nik Norliati Fitri Md Nor , Zulfaqar Sa’adi

This scoping review explores the progress and applications of space-borne remote sensing within the realm of climate change research. It systematically compiles significant advancements in remote sensing technology, with a focus on its application for tracking diverse indicators of climate change. The review performs a thorough examination of various sensor types and technologies, evaluates the challenges and limitations encountered, and considers methods to overcome these obstacles. By adopting an integrated and multidisciplinary approach, the study connects the gap between technological progress and its policy implications, alongside mitigation and adaptation strategies. This offers a holistic view of the pivotal role of remote sensing in the study of climate change, providing valuable insights for researchers, policymakers, and practitioners alike.

本文综述了星载遥感在气候变化研究领域的进展和应用。它系统地汇编了遥感技术方面的重大进展，重点是遥感技术在跟踪各种气候变化指标方面的应用。该综述对各种传感器类型和技术进行了彻底的检查，评估了遇到的挑战和限制，并考虑了克服这些障碍的方法。通过采用综合的多学科方法，该研究将技术进步与其政策影响之间的差距与缓解和适应战略联系起来。这为遥感在气候变化研究中的关键作用提供了一个整体的观点，为研究人员、政策制定者和实践者提供了有价值的见解。

引用次数: 0

Assessment of the hydrogeological potential of the north-eastern sector of the town of Dschang (West Cameroon) using integrated remote sensing, geophysics and multi-criteria analysis 利用综合遥感、地球物理和多标准分析评估Dschang镇（喀麦隆西部）东北地区的水文地质潜力

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-07-23 DOI: 10.1016/j.ejrs.2025.07.003

Kenfack Jean Victor, Talla Toteu Rodrigue, Bomeni Isaac Yannick, Demanou Messe Malick Rosvelt, Tchomtchoua Tagne Stéphane, Djoumete Kengni Annie Christelle, Kengni Lucas

This study focuses on the hydrogeological mapping of Dschang, western Cameroon, where drinking water shortages persist due to limited understanding of local aquifers. The research integrates remote sensing, geophysics, and multi-criteria analysis to assess groundwater potential. Key findings include the identification of a primary fracturing network (directions N 20°–30°E and N 60°–70°E) and three distinct resistivity domains based on vertical electrical soundings carried out on 120 points. The resistivity values range from 1.43 to 2467.429 Ω.m, classified as conductive, less conductive, or resistant domains. Hydraulic parameters such as conductivity (0.0036–116.0073 m/day), porosity (0.192–46.894 %), transmissivity (0.019–1507.817 m²/day), and aquifer thickness (2–63 m) were analyzed. Using multi-criteria analysis, the data were synthesized to produce a hydrogeological map. Highly favorable zones for groundwater exploitation are concentrated in basaltic and ignimbritic formations in the north and south of the study area, while moderately favorable zones surround these areas. Unfavorable zones are located in the center and southern periphery.

这项研究的重点是喀麦隆西部Dschang的水文地质测绘，由于对当地含水层的了解有限，那里的饮用水短缺问题仍然存在。该研究综合了遥感、地球物理和多准则分析来评估地下水潜力。主要发现包括确定了一个主压裂网络（北纬20°-30°E和北纬60°-70°E），以及基于在120个点上进行的垂直电测深的三个不同的电阻率域。电阻率取值范围为1.43 ~ 2467.429 Ω。M，分为导电，不导电或电阻域。分析了含水层电导率（0.0036-116.0073 m/day）、孔隙度（0.192-46.894 %）、透光率（0.019-1507.817 m2/day）、含水层厚度（2-63 m）等水力参数。采用多准则分析方法，对资料进行综合处理，生成水文地质图。研究区北部和南部的玄武岩组和火成岩组为地下水开发的高度有利带，而这些区域的周围为中等有利带。不利区域位于中心和南部边缘。

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引用次数: 0

Artificial intelligence enabled spectral-spatial feature extraction techniques for land use and land cover classification using hyperspectral images – An inclusive review 使用高光谱图像进行土地利用和土地覆盖分类的人工智能光谱空间特征提取技术-综述

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-07-18 DOI: 10.1016/j.ejrs.2025.06.004

V. Sangeetha, L. Agilandeeswari

The growth of artificial intelligence techniques such as machine learning and deep learning facilitates the hyperspectral image processing applicable in developing various remote sensing applications such as Change detection in Land Use and Land Cover (LULC) classification, Evaluation of the nutritional content, and health of the crops in Agriculture. However, Hyperspectral imaging is frequently utilized in remote sensing and earth observation applications to identify environmental changes. One of the key tasks in hyperspectral image classification is feature extraction. This paper gives a comprehensive review of the recent hyperspectral image feature extraction techniques for LULC. This study aims to identify the open issues, research challenges, and future directions that will help researchers develop efficient feature extraction techniques for better LULC hyperspectral image classification. The performance of the state-of-the-art feature extraction techniques for hyperspectral images is analyzed in terms of the overall accuracy, average accuracy, and kappa coefficient across the benchmark datasets, namely Indian Pines, Pavia dataset, and Salinas dataset. From the analysis, we observe that in all the benchmark datasets, the framework 2D + 3D CNN with spectral-spatial integration not only extracts the comprehensive features but also increases the classification accuracy with less computational complexity compared to other competing frameworks. Both 2D CNNs and 3D CNNs are utilized for extracting features and patterns from data with multiple spectral bands, and each architecture has its advantages and challenges. 2D CNNs are more common and computationally efficient, while 3D CNNs capture spatial-spectral correlations more directly.

机器学习和深度学习等人工智能技术的发展促进了高光谱图像处理，可用于开发各种遥感应用，如土地利用和土地覆盖变化检测（LULC）分类、农业作物营养成分评估和健康状况评估。然而，高光谱成像在遥感和地球观测应用中经常被用于识别环境变化。高光谱图像分类的关键任务之一是特征提取。本文综述了近年来用于LULC的高光谱图像特征提取技术。本研究旨在确定开放的问题、研究挑战和未来的方向，这将有助于研究人员开发有效的特征提取技术，以更好地进行LULC高光谱图像分类。从总体精度、平均精度和kappa系数三个方面分析了目前最先进的高光谱图像特征提取技术在基准数据集（即Indian Pines、Pavia和Salinas数据集）中的性能。通过分析，我们发现在所有的基准数据集中，与其他竞争框架相比，具有光谱-空间集成的2D + 3D CNN框架不仅提取了综合特征，而且在计算复杂度较低的情况下提高了分类精度。二维cnn和三维cnn都用于从多光谱波段的数据中提取特征和模式，每种架构都有其优势和挑战。2D cnn更常见，计算效率更高，而3D cnn更直接地捕获空间-光谱相关性。

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引用次数: 0

A novel weighted average ensemble method for landslide susceptibility mapping: A case study in Yuanyang, China 一种新的加权平均集合方法在滑坡易感性制图中的应用——以元阳为例

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-07-14 DOI: 10.1016/j.ejrs.2025.07.002

Valisoasarobidy José Gabriel , Ruihong Wang , Doshrot Mahato , Can Wei

Landslide susceptibility mapping is critical for risk assessment, but existing ensemble methods like VotingClassifier suffer from three unresolved limitations: static weight allocation that ignores spatial variability, lack of quantifiable uncertainty measures, and poor integration of interpretability tools. This study introduces a novel weighted average ensemble method that dynamically adjusts weights for Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) through 5-fold spatial cross-validation, improving prediction robustness across Yuanyang County’s 2240 km² of mountainous terrain (23°05′–23°15′N, 102°40′–102°50′E) with 817 validated landslides. The method tackles important issues by combining the best features of strong models while reducing the effects of related variables using composite indices (like a soil-lithology index based on a Pearson correlation of r = 0.81), backed by a thorough preprocessing process that includes Moran’s I-validated stratified sampling (I = 0.12), normalization that accounts for outliers (95th percentile), and spatial division with 500 m buffers. The novel ensemble achieved an accuracy of 84.32 % and an ROC AUC of 91.96 %, with sensitivity analysis via SHAP (SHapley Additive exPlanations) identifying rainfall (21 %), distance index (13 %), and elevation slope index (27 %) as dominant drivers, while uncertainty analysis revealed prediction intervals of ±0.62 width (95 % coverage). The resulting maps, validated through spatial consistency checks (AUC > 0.84), provide actionable tools for high-risk zones. This research improves landslide susceptibility mapping by developing a dynamic, uncertainty-based system that rectifies major weaknesses in static ensemble methods, thereby establishing a replicable standard for future investigations.

滑坡敏感性制图对于风险评估至关重要，但现有的集成方法（如VotingClassifier）存在三个未解决的限制：忽略空间变异性的静态权重分配，缺乏可量化的不确定性度量，以及对可解释性工具的集成能力差。本文提出了一种新的加权平均集成方法，通过5倍空间交叉验证，动态调整随机森林（RF）、支持向量机（SVM）和极端梯度增强（XGBoost）的权重，提高了对远阳县2240 km2山地地形（23°05′-23°15′n, 102°40′-102°50′e） 817个已验证滑坡的预测鲁棒性。该方法通过结合强模型的最佳特征来解决重要问题，同时使用复合指数（如基于r = 0.81的Pearson相关性的土壤-岩石指数）减少相关变量的影响，并辅以彻底的预处理过程，包括Moran的I验证分层抽样（I = 0.12），考虑异常值的归一化（第95百分位数），以及500 m缓冲区的空间划分。新集合的准确度为84.32%，ROC AUC为91.96%，通过SHapley加性解释（SHapley Additive exPlanations）进行敏感性分析，确定降雨（21%）、距离指数（13%）和高程坡度指数（27%）是主要驱动因素，而不确定性分析显示预测区间为±0.62宽度（95%覆盖率）。生成的地图，通过空间一致性检查(AUC >；0.84)，为高风险地区提供可操作的工具。本研究通过开发一个动态的、基于不确定性的系统来改进滑坡易感性制图，该系统纠正了静态集合方法的主要弱点，从而为未来的调查建立了可复制的标准。

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引用次数: 0

Assessing water quality of a lake using combination of drone images and artificial intelligence models 结合无人机图像和人工智能模型对湖泊水质进行评估

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-07-08 DOI: 10.1016/j.ejrs.2025.07.001

Nawras Shatnawi , Hani Abu-Qdais , Muna Abu-Dalo , Eman Khalid Salem

Lakes serve as a source of water to meet the demand of various sectors such as urban, agricultural and recreational sectors. The purpose of this paper is to investigate the capability of using combination of multispectral drone imagery with machine learning algorithm for the assessment of water quality in an artificial lake at the Jordan University of Science and Technology (JUST) campus. Several images with different resolutions under different wavebands were captured with DJI Phantom-4 drone equipped with sensors in the blue green, red, Red Edge, and Near Infrared. At the same time water samples were also collected from ten different points in the lake to analyze physical and chemical quality parameters. The spectral reflection was used to calculate multiple water body indices, and the resulting indices were correlated to water quality parameters. The indices with coefficient of determination greater than 0.7 were used to develop various artificial intelligence models (AI) such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Gradient Boosted Decision Trees (GBDT), Generalized Linear Model (GLM) and Artificial Neural Network (ANN). The results showed that among the tested models autoregressive with exogenous (NARX) ANN model has the highest prediction accuracy based on the coefficient of determination (R²) of 0.95 and relative error of 0.034. Comparison of the simulated results indicated the variability of water quality parameters with seasons and inversion accuracy was highest during the summer season. Such an approach offers a useful tool for decision-making to manage lake water quality. Future studies should include more parameters and using hyperspectral sensors for investigating quality parameters of similar water bodies.

湖泊是满足城市、农业和娱乐等各个部门需求的水源。本文的目的是研究将多光谱无人机图像与机器学习算法相结合用于约旦科技大学（JUST）校园人工湖水质评估的能力。大疆幻影-4无人机搭载蓝绿、红、红边、近红外传感器，在不同波段拍摄了多幅不同分辨率的图像。同时，从湖泊的10个不同地点采集水样，分析其理化质量参数。利用光谱反射计算多个水体指数，并将所得指数与水质参数进行关联。这些决定系数大于0.7的指标被用于开发支持向量机（SVM）、随机森林（RF）、决策树（DT）、梯度增强决策树（GBDT）、广义线性模型（GLM）和人工神经网络（ANN）等各种人工智能模型。结果表明，自回归外生（NARX）神经网络模型预测精度最高，决定系数（R2）为0.95，相对误差为0.034。模拟结果表明，夏季水质参数的季节变异性和反演精度最高。该方法为湖泊水质管理的决策提供了有用的工具。未来的研究应包括更多的参数，并利用高光谱传感器来研究类似水体的质量参数。

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引用次数: 0

Slope stability and disaster mechanisms in the Honghe Hani Terraces: a systematic review 红河哈尼阶地边坡稳定性与灾害机制系统综述

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-06-27 DOI: 10.1016/j.ejrs.2025.06.003

Valisoasarobidy José Gabriel , Ruihong Wang , Doshroth Mahato , Can Wei

Slope stability and disaster mechanisms are critical concerns for the Honghe Hani Terraces (HHT), a UNESCO World Heritage Site renowned for its unique agricultural and cultural heritage. This systematic review examines the factors influencing slope instability, the role of climatic conditions, and the impact of agricultural practices in the region. Using the PRISMA framework, 105 studies from 2000 to 2023 were analyzed, identifying key trends and research gaps through bibliometric and thematic analyses. The findings reveal that natural factors, such as rainfall intensity and soil properties, interact with anthropogenic factors, including land use changes and traditional farming practices, to significantly influence slope stability. While traditional agricultural techniques like terracing can enhance soil conservation, improper management and recent land use changes, such as deforestation and urbanization, have intensified instability. Numerical simulations highlight the complex interplay between rainfall, irrigation, and slope dynamics, emphasizing the need for integrated management strategies. The review underscores the importance of combining traditional knowledge with modern technologies, such as remote sensing and GIS, to develop sustainable land management practices and early warning systems. Community involvement and capacity-building are also essential for effective mitigation. Despite limitations, such as methodological variability and data inconsistencies, this review provides a comprehensive understanding of slope stability in the HHT and proposes future research directions to enhance disaster resilience and preserve this unique cultural landscape.

红河哈尼梯田以其独特的农业和文化遗产而闻名于世，其边坡稳定性和灾害机制是红河哈尼梯田的关键问题。本系统综述考察了影响边坡不稳定的因素、气候条件的作用以及该地区农业实践的影响。利用PRISMA框架，对2000年至2023年的105项研究进行了分析，通过文献计量学和专题分析确定了关键趋势和研究差距。研究结果表明，降雨强度和土壤性质等自然因素与土地利用变化和传统耕作方式等人为因素相互作用，对边坡稳定性产生显著影响。虽然梯田等传统农业技术可以加强土壤保持，但管理不当和最近的土地利用变化，如森林砍伐和城市化，加剧了不稳定。数值模拟强调了降雨、灌溉和边坡动态之间复杂的相互作用，强调了综合管理策略的必要性。该审查强调了将传统知识与遥感和地理信息系统等现代技术结合起来以发展可持续土地管理做法和早期预警系统的重要性。社区参与和能力建设对于有效缓解也是必不可少的。尽管存在方法差异和数据不一致等局限性，但本文提供了对HHT边坡稳定性的全面理解，并提出了未来的研究方向，以增强灾害恢复能力并保护这一独特的文化景观。

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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 : 2025-06-26 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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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 : 2025-06-17 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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