Egyptian Journal of Remote Sensing and Space Sciences最新文献_第4页

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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引用次数: 0

Impact of hydrological mass loading using GRACE/GRACE-FO gravity products and GNSS data over Egypt 利用GRACE/GRACE- fo重力产品和GNSS数据对埃及水文质量负荷的影响

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-29 DOI: 10.1016/j.ejrs.2025.05.010

Ahmed Saadon , Basem Elsaka , Mohamed El-Ashquer , Ashraf El-Kotb Mousa , Gamal El-Fiky

This study investigates the impact of hydrological mass loading on the Egyptian Permanent GNSS Network (EPGN) stations. Initially, GRACE and GRACE-FO products are evaluated, resulting in selecting the CSR center’s DDK5 monthly solutions for estimating terrestrial total water storage (TWS) in terms of equivalent water height (EWH). Monthly vertical displacements (VD) rates are calculated using GNSS data from EPGN stations, while TWS in terms of EWH is derived from GRACE/GRACE-FO data and WGHM model at the same locations. The findings from GRACE show that the mean monthly EWH values exhibit a negative trend of −2.36 mm/year from 2002 to 2012, followed by a positive trend of 3.94 mm/year from early 2013 until mid-2017. For GRACE-FO solutions, EWH shows a positive trend of 5.69 mm/year from mid-2018 to early 2024. A comparison of mean monthly EWH variations from GRACE/GRACE-FO and WGHM with GNSS-derived VD demonstrates a negative correlation at most GNSS stations, particularly in areas with significant hydrological signals, such as the Egyptian Delta and Lake Nasser. This emphasizes the impact of hydrological mass changes on these stations. Finally, mean monthly EWHs from GRACE are evaluated against the WGHM over Egypt. In addition, water level heights are compared to the EWHs from GRACE and WGHM at the ABSM station near Lake Nasser. Results show good agreement between EWHs estimated from GRACE and the WGHM over Egypt. At ABSM station, the water level heights of Lake Nasser provide robustness of our findings.

本研究调查了水文质量负荷对埃及永久GNSS网络（EPGN）站的影响。首先，对GRACE和GRACE- fo产品进行评估，最终选择CSR中心的DDK5月度解决方案，以等效水高（EWH）估算陆地总储水量（TWS）。月垂直位移（VD）率使用来自EPGN站点的GNSS数据计算，而EWH的TWS则来自同一地点的GRACE/GRACE- fo数据和WGHM模型。GRACE观测结果表明，2002 - 2012年，月平均EWH值呈现负变化趋势（- 2.36 mm/年），2013年初至2017年年中，月平均EWH值呈现正变化趋势（3.94 mm/年）。对于GRACE-FO解决方案，从2018年年中到2024年初，EWH呈5.69毫米/年的正趋势。GRACE/GRACE- fo和WGHM的月平均EWH变化与GNSS衍生的VD的比较表明，在大多数GNSS站，特别是在具有重要水文信号的地区，如埃及三角洲和纳赛尔湖，EWH变化呈负相关。这强调了水文质量变化对这些站的影响。最后，根据埃及的WGHM对GRACE的月平均EWHs进行了评估。此外，在Nasser湖附近的ABSM站，将水位高度与GRACE和WGHM的EWHs进行了比较。结果表明，GRACE估计的EWHs与埃及WGHM的EWHs吻合良好。在ABSM站，纳赛尔湖的水位高度为我们的发现提供了稳健性。

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引用次数: 0

Flood mapping and impact analysis by fusion of remote sensing and open geospatial data: Sindh case study 基于遥感与开放地理空间数据融合的洪水制图与影响分析：信德省案例研究

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-28 DOI: 10.1016/j.ejrs.2025.05.001

Munazza Usmani , Hafiz Muhammad Tayyab Bhatti , Riccardo Nanni , Francesca Bovolo , Maurizio Napolitano

Flooding remains one of the most severe natural hazards in Pakistan, consistently leading to substantial losses in lives, livelihoods, and infrastructure. The country has experienced recurring flood events, with their frequency and intensity increasingly influenced by shifting climate patterns and irregular rainfall. The phenomena got worse over time and in 2022 all provinces of the country were severely impacted. The damage and impact of a flood may be detected, determined, and estimated with the use of remote sensing and available open geographic information system data. This study presents a scalable, efficient flood mapping framework that leverages freely available multi-source satellite data and open geospatial datasets to assess flood impact with high spatial detail. Multisource satellite imagery was utilized to detect inundation extents. Pre-processing of the remote sensing data was conducted using Google Earth Engine, and spatial integration of data layers for flood mapping was performed in ArcGIS. The results demonstrate that the 2022 Pakistan flood was the worst environmental disaster in history. The flood submerged a total area of nearly 25,000 km

^{2}

in the Sindh province, destroying 14,558 villages and leaving behind a trail of devastation. The methodology enables rapid, repeatable, and cost-effective flood damage assessment and is transferable to other regions. By combining cloud-based processing with open data, this framework supports timely decision-making for disaster response, prevention, and policy planning.

洪灾仍然是巴基斯坦最严重的自然灾害之一，不断给人们的生命、生计和基础设施造成重大损失。该国经历了反复发生的洪水事件，其频率和强度越来越多地受到气候模式变化和不规则降雨的影响。随着时间的推移，这种现象越来越严重，到2022年，全国所有省份都受到了严重影响。利用遥感和可用的开放地理信息系统数据，可以探测、确定和估计洪水的破坏和影响。本研究提出了一个可扩展的、高效的洪水制图框架，该框架利用可免费获得的多源卫星数据和开放的地理空间数据集，以高空间细节评估洪水影响。利用多源卫星图像检测淹没程度。利用谷歌Earth Engine对遥感数据进行预处理，并在ArcGIS中进行数据层空间整合进行洪水制图。结果表明，2022年巴基斯坦洪水是历史上最严重的环境灾害。洪水淹没了信德省近2.5万平方公里的面积，摧毁了14558个村庄，留下了一片废墟。该方法能够快速、可重复、具有成本效益的洪水灾害评估，并可转移到其他地区。通过将基于云的处理与开放数据相结合，该框架支持对灾害响应、预防和政策规划进行及时决策。

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引用次数: 0

Integrated point-line-network model for road extraction based on tree-shaped point process 基于树形点过程的道路提取综合点-线-网模型

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-27 DOI: 10.1016/j.ejrs.2025.05.009

You Wu , Chen Wang

Unsupervised road extraction methods based on traditional point processes have long faced challenges such as bottlenecks in processing efficiency and deficiencies in topological connectivity. To improve these drawbacks, this study proposes a new integrated modeling framework. First step is to construct the integrated point-line-network model based on the tree-shaped point process, in which the relationships between point, line and network are supposed to be constrained according to topological structure features like branching, trend, connectivity of road. In second step, integrated point-line-network model is further constrained by spectral Gaussian mixture model and Kullback-Leibler divergence of road, and then extraction model is obtained. Third step is to redesign transfer kernels of Reversible Jump Markov Chain Monte Carlo (RJMCMC) for simulation and optimization of road extraction. Finally, different scales of sub-meter-level remote sensing images are tested, and the results show that efficiency of the proposed method is higher than traditional methods, and the connectivity is well maintained.

基于传统点过程的无监督道路提取方法一直面临着处理效率瓶颈和拓扑连通性不足等挑战。为了改善这些缺陷，本研究提出了一种新的集成建模框架。第一步是基于树形点过程构建点-线-网一体化模型，根据道路的分支、走向、连通性等拓扑结构特征约束点、线、网之间的关系。第二步，利用高斯混合谱模型和道路的Kullback-Leibler散度对点-线-网综合模型进行约束，得到提取模型；第三步，重新设计可逆跳跃马尔可夫链蒙特卡罗（RJMCMC）的传递核，用于道路提取的仿真和优化。最后，对不同尺度的亚米级遥感图像进行了测试，结果表明，该方法的效率高于传统方法，且保持了良好的连通性。

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引用次数: 0

GNSS geodetic velocity prediction using ensemble tree models in Abu-Dabbab, Egypt 利用集合树模型在埃及阿布达巴布进行GNSS大地测速预测

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-21 DOI: 10.1016/j.ejrs.2025.05.008

Alshimaa Y. Abo Gharbia , Ahmed Gomaa , Mohamed Saleh , Ashraf Elkutb Mousa , Ibrahim Atiatallah Abbas , Moatamad R. Hassan

Estimating Global Navigation Satellite System (GNSS) velocities is essential for understanding crustal deformation and motion. This work employs the Random Forest (RF) and Gradient Boosting Machines (GBM), two machine learning (ML) techniques, to estimate horizontal velocities at specific locations using GNSS data. Crustal deformation data were acquired through Global Positioning System (GPS) techniques, with positions of eleven stations determined from eight GPS measurement campaigns. Eighty percent of the GNSS velocity data from stations in the Abu-Dabbab region were used for training, while twenty percent were reserved for testing the models. RF demonstrated superior performance in estimating east geodetic GPS velocities with the lowest mean absolute error (MAE), while GBM excelled in predicting north geodetic GPS velocities, also achieving the lowest MAE. The maximum differences between model-predicted and reference velocities were 0.09 mm/year for RF and 0.1 mm/year for GBM, underscoring the precision of these methods. Despite data constraints the study confirms the efficacy of ML techniques, particularly RF and GBM, in providing accurate GNSS velocity estimates.

估计全球导航卫星系统（GNSS）的速度对于了解地壳变形和运动至关重要。这项工作采用随机森林（RF）和梯度增强机（GBM）这两种机器学习（ML）技术，使用GNSS数据估计特定位置的水平速度。通过全球定位系统（GPS）技术获得了地壳变形数据，其中11个站点的位置由8次GPS测量活动确定。来自Abu-Dabbab地区站点的80%的GNSS速度数据用于训练，而20%用于测试模型。RF算法在预测东线GPS速度方面表现优异，平均绝对误差（MAE）最低，而GBM算法在预测北线GPS速度方面表现优异，平均绝对误差（MAE）最低。模型预测速度与参考速度的最大差异为：RF为0.09 mm/年，GBM为0.1 mm/年，强调了这些方法的精度。尽管数据有限，但研究证实了机器学习技术，特别是射频和GBM技术，在提供准确的GNSS速度估计方面的有效性。

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引用次数: 0

Enhancing motion compensation in spaceborne SAR imaging 增强星载SAR成像中的运动补偿

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-16 DOI: 10.1016/j.ejrs.2025.05.005

Shimaa Abd El-Monem , Ahmed Azouz , Alaaeldin S. Hassan , El-Sayed Soliman A. Said , Abdelhady A. Ammar

Synthetic Aperture Radar (SAR) is a widely utilized remote sensing technology, offering robust operational efficiency under all weather conditions and independent of daylight. Ideally, the SAR platform maintains a linear trajectory at a constant altitude and velocity. However, this idealization is compromised for spaceborne SAR systems, such as those in low Earth orbit (LEO), due to the satellite’s elliptical orbit, which introduces motion errors that degrade image focusing quality. This paper presents a novel approach to enhance first-order motion compensation (MOCO) by addressing the motion errors caused by elliptical orbital dynamics and perturbations. The proposed methodology involves applying three distinct fitting techniques to the invariant range error, a critical parameter in first-order MOCO, and optimizing phase gradients to determine the optimal coefficients for improving image quality metrics. Real-raw SAR data from the Sentinel-1 Level-0 dataset is processed to validate the proposed techniques, and the results are benchmarked against the corresponding Sentinel-1 Level-1 Single Look Complex (SLC) image. The validation is conducted through two approaches: first, image quality assessment using sharpness, contrast, and entropy metrics; and second, quantitative evaluation of azimuth-integrated sidelobe ratio (AISLR), azimuth peak sidelobe ratio (APSLR), and impulse response width (IRW) at two prominent reflective points. The findings indicate a marked enhancement in the image quality parameters, demonstrating the efficacy of the proposed motion compensation and optimization framework.

合成孔径雷达（SAR）是一种广泛应用的遥感技术，在全天候和不受日光影响的情况下提供强大的操作效率。理想情况下，SAR平台在恒定的高度和速度下保持线性轨迹。然而，由于卫星的椭圆轨道引入了运动误差，降低了图像聚焦质量，因此这种理想化的效果在星载SAR系统（如低地球轨道）中受到了损害。本文提出了一种新的方法，通过解决椭圆轨道动力学和摄动引起的运动误差来增强一阶运动补偿。提出的方法包括应用三种不同的拟合技术来处理不变距离误差，一阶MOCO中的一个关键参数，以及优化相位梯度以确定提高图像质量指标的最佳系数。对来自Sentinel-1 Level-0数据集的真实原始SAR数据进行处理以验证所提出的技术，并将结果与相应的Sentinel-1 Level-1 Single Look Complex （SLC）图像进行基准测试。通过两种方法进行验证：首先，使用清晰度，对比度和熵指标进行图像质量评估；定量评价两个突出反射点的方位角积分旁瓣比（AISLR）、方位角峰值旁瓣比（APSLR）和脉冲响应宽度（IRW）。结果表明，图像质量参数显著增强，证明了所提出的运动补偿和优化框架的有效性。

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引用次数: 0

Employing both full and partial sub-pixel mapping methods to delineate hydrothermal alteration zones associated with porphyry copper deposits 采用全亚像素和部分亚像素填图方法圈定了与斑岩铜矿相关的热液蚀变带

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-15 DOI: 10.1016/j.ejrs.2025.05.007

Yousef Bahrami, Hossein Hassani, Abbas Maghsoudi

The southeastern portion of the Urumieh–Dokhtar magmatic arc (UDMA), known as Kerman Cenozoic magmatic arc (KCMA), is a major host to world-class giant to subeconomic small porphyry copper deposits (PCDs) in Iran. As the KCMA is characterized by well-exposed rocks and sparsely vegetated surfaces, it is an intriguing region for geological remote sensing studies. In particular, mixed pixels are a key source of annoyance in traditional image classification because of a sensor’s immediate field of view restriction and the variety of land cover classes. By evaluating the observed spectrum of mixed pixels, sub-pixel mapping techniques can decompose each mixed pixel and determine the proportion of each component class, and so a classification map with a finer resolution is attainable. This paper endeavors to assess the capability and accuracy of linear spectral unmixing (LSU), multiple endmember spectral mixture analysis (MESMA), and mixture tuned target constrained interference minimized filter analysis (MTTCIMF) to investigate how well these sub-pixel algorithms could identify and map key hydrothermal alteration zones linked with PCDs in the Pariz–Chahargonbad area. Previous works have applied these algorithms widely to hyperspectral data, but few previous works have applied them to multispectral data such as ASTER. In this work, these algorithms were found helpful in the accurate identification of argillic, phyllic, and propylitic alteration zones per validation with field observations, petrographic studies and X-ray diffraction analysis of rock samples.

乌鲁木齐-多赫塔尔岩浆弧（UDMA）的东南部，被称为Kerman新生代岩浆弧（KCMA），是伊朗世界级巨型到亚经济小型斑岩铜矿（PCDs）的主要矿床。由于KCMA具有暴露良好的岩石和稀疏植被表面的特点，因此它是一个有趣的地质遥感研究区域。特别是，由于传感器的直接视场限制和土地覆盖类别的多样性，混合像素是传统图像分类的一个主要烦恼来源。亚像元映射技术通过评估混合像元的观测光谱，对每个混合像元进行分解，确定各成分类的比例，从而获得更精细的分类图。本文通过对线性光谱分解（LSU）、多端元光谱混合分析（MESMA）和混合调谐目标约束干涉最小化滤波分析（MTTCIMF）的能力和精度进行评估，探讨这些亚像素算法在parizz - chahargonbad地区识别和绘制与PCDs相关的关键热液蚀变带的效果。以往的研究已经将这些算法广泛应用于高光谱数据，但很少有研究将其应用于ASTER等多光谱数据。通过野外观测、岩石学研究和岩石样品的x射线衍射分析，发现这些算法有助于准确识别泥质、叶质和丙质蚀变带。

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