Egyptian Journal of Remote Sensing and Space Sciences最新文献

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

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

{"title":"Evaluating the factors affecting landslides using machine learning algorithms (case study: the catchment area of Karun-3 Dam, Iran)","authors":"Rahman Zandi , Ghasem Shah Pari Far","doi":"10.1016/j.ejrs.2025.07.005","DOIUrl":"10.1016/j.ejrs.2025.07.005","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 512-522"},"PeriodicalIF":4.1,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

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

{"title":"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","authors":"Kenfack Jean Victor, Talla Toteu Rodrigue, Bomeni Isaac Yannick, Demanou Messe Malick Rosvelt, Tchomtchoua Tagne Stéphane, Djoumete Kengni Annie Christelle, Kengni Lucas","doi":"10.1016/j.ejrs.2025.07.003","DOIUrl":"10.1016/j.ejrs.2025.07.003","url":null,"abstract":"<div><div>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 m<sup>2</sup>/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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 484-511"},"PeriodicalIF":3.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Artificial intelligence enabled spectral-spatial feature extraction techniques for land use and land cover classification using hyperspectral images – An inclusive review","authors":"V. Sangeetha, L. Agilandeeswari","doi":"10.1016/j.ejrs.2025.06.004","DOIUrl":"10.1016/j.ejrs.2025.06.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 455-467"},"PeriodicalIF":3.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"A novel weighted average ensemble method for landslide susceptibility mapping: A case study in Yuanyang, China","authors":"Valisoasarobidy José Gabriel , Ruihong Wang , Doshrot Mahato , Can Wei","doi":"10.1016/j.ejrs.2025.07.002","DOIUrl":"10.1016/j.ejrs.2025.07.002","url":null,"abstract":"<div><div>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 km<sup>2</sup> 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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 436-454"},"PeriodicalIF":3.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Assessing water quality of a lake using combination of drone images and artificial intelligence models","authors":"Nawras Shatnawi , Hani Abu-Qdais , Muna Abu-Dalo , Eman Khalid Salem","doi":"10.1016/j.ejrs.2025.07.001","DOIUrl":"10.1016/j.ejrs.2025.07.001","url":null,"abstract":"<div><div>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 (R<sup>2</sup>) 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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 426-435"},"PeriodicalIF":3.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Slope stability and disaster mechanisms in the Honghe Hani Terraces: a systematic review","authors":"Valisoasarobidy José Gabriel , Ruihong Wang , Doshroth Mahato , Can Wei","doi":"10.1016/j.ejrs.2025.06.003","DOIUrl":"10.1016/j.ejrs.2025.06.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 411-425"},"PeriodicalIF":3.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Quantitative assessment of spatiotemporal variability in air quality within the Amman-Zarqa urban Area, Jordan","authors":"Abdulla Al-Rawabdeh , Farah Alzu’bi , Ali Almagbile","doi":"10.1016/j.ejrs.2025.06.002","DOIUrl":"10.1016/j.ejrs.2025.06.002","url":null,"abstract":"<div><div>Many factors influence the concentration of air pollutants, particularly Carbon Monoxide (CO) and Nitrogen Dioxide (NO<sub>2</sub>). This research aims to study the spatiotemporal variability of CO and NO<sub>2</sub> 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 NO<sub>2</sub> 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, NO<sub>2</sub> 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 r<sup>2</sup> 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 NO<sub>2</sub>, 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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 395-410"},"PeriodicalIF":3.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Assessing groundwater storage variations in the Volta River Basin combining remote sensing tools and machine learning downscaling techniques","authors":"Randal Djima Djessou , Xiaoyun Wan , Richard Fiifi Annan , Abdoul-Aziz Bio Sidi D. Bouko","doi":"10.1016/j.ejrs.2025.06.001","DOIUrl":"10.1016/j.ejrs.2025.06.001","url":null,"abstract":"<div><div>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 R<sup>2</sup> (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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 3","pages":"Pages 383-394"},"PeriodicalIF":3.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Impact of hydrological mass loading using GRACE/GRACE-FO gravity products and GNSS data over Egypt","authors":"Ahmed Saadon , Basem Elsaka , Mohamed El-Ashquer , Ashraf El-Kotb Mousa , Gamal El-Fiky","doi":"10.1016/j.ejrs.2025.05.010","DOIUrl":"10.1016/j.ejrs.2025.05.010","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 370-382"},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0