Sentinel-2 Satellite Images Research Articles

Dynamics of equilibrium line altitude in glaciers of the Monte Rosa massif in the Alps derived from Sentinel-2 satellite images

Dynamics of equilibrium line altitude in glaciers of the Monte Rosa massif in the Alps derived from Sentinel-2 satellite images

A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat

Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data.

Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.

This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on visual features in the satellite imagery that covers each tract. The study utilizes Sentinel-2 satellite images, processed using the ResNet-50 DCNN, to extract deep neural visual features (DNVF). Obesity prevalence data, sourced from the CDC's 2022 estimates, is analyzed at the census tract level. The datasets were integrated to apply a machine learning model to predict the obesity rates in 1052 different census tracts in Missouri. The analysis reveals significant associations between DNVF and obesity prevalence. The predictive models show moderate success in estimating and predicting obesity rates in various census tracts within Missouri. The study emphasizes the potential of using satellite imagery and advanced machine learning in public health research. It points to environmental factors as significant determinants of obesity, suggesting the need for targeted health interventions. Employing DNVF to explore and predict obesity rates offers valuable insights for public health strategies and calls for expanded research in diverse geographical contexts.

Comparative Assessment of Different Architectures of Convolutional Neural Network for Semantic Segmentation of Forest Disturbances from Multi-Temporal Satellite Images

Algorithms based on convolutional neural networks are the most efficient for semantic segmentation of images, including segmentation of forest cover disturbances from satellite images. In this study, we consider the applicability of various modifications of the U-net architecture of convolutional neural network for recognizing logged, burnt and windthrow areas in forests from multi-temporal and multi-seasonal Sentinel-2 satellite images. The assessment was carried out on three test sites that differ significantly in the characteristics of forest stands and forest management. The highest accuracy (average F-measure of 0.59) was obtained from the U-net model, while the models that showed the best results during training (Attention U-Net and MobilNetv2 U-Net) did not improve segmentation of independent data. The resulting accuracy estimates are close to those previously published for forests with a substantial proportion of selective logged areas. Characteristics of logged areas and windthrows, namely their area and type are the main factor determining the accuracy of semantic segmentation. Substantial differences were also revealed between the images taken in different seasons of the year, with the maximum segmentation accuracy based on winter pairs of images. According to summertime and different-season pairs of images, the area of forest disturbances is substantially underestimated. Forest species composition has a less significant effect, although for two of the three test sites, the maximum accuracy was observed in dark coniferous forests, and the minimum in deciduous forests. There was no statistically significant effect of slope lighting factor calculated from digital elevation model on segmentation accuracy based for winter pairs of images. The accuracy of segmentation of burnt areas, which was assessed using the example of 14 large forest fires in 2021-2022, is unsatisfactory, which is probably due to the varying degrees of damage to the forest cover in the burnt areas.

Mapping Crop Types for Beekeepers Using Sentinel-2 Satellite Image Time Series: Five Essential Crops in the Pollination Services

Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, this study aimed to develop DL-based classification models for mapping five essential crops in pollination services in Quebec province, Canada, by using Sentinel-2 SITS. Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1DTempCNNs), one-dimensional spectral CNNs (1DSpecCNNs), and long short-term memory (LSTM). Accordingly, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1DTempCNN and LSTM models, and spectral features using the 1DSpecCNN model. Our findings indicated that the LSTM model (macro-averaged recall of 0.80, precision of 0.80, F1-score of 0.80, and ROC of 0.89) outperformed both 1DTempCNNs (macro-averaged recall of 0.73, precision of 0.74, F1-score of 0.73, and ROC of 0.85) and 1DSpecCNNs (macro-averaged recall of 0.78, precision of 0.77, F1-score of 0.77, and ROC of 0.88) models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using Sentinel-2 SITS. Furthermore, applying one-dimensional convolution (Conv1D) across the spectral domain demonstrated greater potential in distinguishing land covers and crop types than applying it across the temporal domain. This study contributes to providing insights into the capabilities and limitations of various DL-based classification models for crop mapping using Sentinel-2 SITS.

Combining radiative transfer model with statistical model for distinguishing heavy metal stress level in rice from Sentinel-2 satellite images

Combining radiative transfer model with statistical model for distinguishing heavy metal stress level in rice from Sentinel-2 satellite images

Towards Nationwide Mapping of Seagrasses in the Philippines: Empowering Students through Remote Sensing Education

Abstract. Seagrasses are critical marine ecosystems facing numerous threats in the Philippines. This study aimed to develop a robust methodology for nationwide seagrass mapping using open-access satellite imagery and empower students to contribute through remote sensing education. We achieved this through a two-pronged approach: (1) developing a satellite-based mapping methodology and (2) training students to apply this method across the archipelago. Utilizing Sentinel-2 satellite images, we processed the data using SNAP and Google Earth Engine to identify seagrass beds. Before classification, the images underwent preprocessing steps including land masking, water column correction, and extraction of shallow water bathymetry. These processed layers were then stacked with Sentinel-2 bands 2, 3, 4, 8, and 11. Two classification methods, support vector machine, and random forest, were employed, and their results were compared with existing seagrass maps. Students achieved overall accuracies ranging from 70 to 90 percent. Prior to mapping activities, students received training on remote sensing techniques and seagrass ecology. This project demonstrates the potential of remote sensing for large-scale seagrass mapping and highlights the value of student engagement in marine conservation.

Optimizing SVM for argan tree classification using Sentinel-2 data: A case study in the Sous-Massa Region, Morocco

The development of efficient classifiers for land cover remains challenging due to the presence of hyperparameters in the model. Conventional approaches rely on manual tuning, which is both time-consuming and impractical, often leading to suboptimal results. This study aimed to optimize the hyperparameters of the Support Vector Machine (SVM) algorithm using the grid search method to map the distribution of the Argan forest in the Souss-Massa region of Morocco from Sentinel-2 satellite image. To achieve this, we examined the C parameter for the linear function, as well as the C and gamma parameters for the radial RBF and sigmoid functions. Similarly, we explored the C, gamma, and degree parameters for the polynomial function chosen using the grid search method. These parameters are compared with the default hyperparameters of each SVM function. The results are validated using the cross-validation method and by the following scores: accuracy, precision, recall, F1 score, and Cohen’s Kappa. The experiments were conducted using the Earth Engine Python API in Google Colab (Google Collaboratory). In addition, experimental results indicate that the hyperparameters selected by grid search yield higher scores than the default hyperparameters. The best results were achieved using the hyperparameters of the polynomial base kernel, specifically with C = 10, degree = 2, and gamma = 10. Accuracy = 96.61%.

Integrated Early Flood Prediction using Sentinel-2 Imagery with VANET-MARL-based Deep Neural RNN

<p style="line-height:200%;"><span style="font-family:"Times New Roman","serif";font-size:12.0pt;line-height:200%;" lang="EN-US">Floods are a major contributor to the destruction of infrastructure and the overall economy of afflicted countries, leading to loss of life and significant damage. Remote sensing, satellite imagery photography, global positioning system, and geographic information system (GIS) are commonly used to identify floods and analyze the associated damages. The research presented here integrates Sentinel-2 satellite images, VANET with Multi-Agent Reinforcement Learning (MARL), and a deep neural RNN for early flood prediction. Sentinel-2 imaging delivers extensive geographical and temporal data regarding land cover and water bodies, while VANET-MARL provides real-time ground-truth information and distributed decision-making capabilities. The Deep Neural RNN efficiently acquires intricate patterns from the combined data to forecast the likelihood, intensity, and scope of floods. The experimental findings clearly show that the suggested system outperforms standard methods in terms of accuracy, precision, recall, and lead time. VANET-MARL integration improves the system's ability to adapt and remain strong in changing circumstances. The results showed that the method was 94.8% accurate in early predicting floods.</span></p>

Provision of land use and forest density maps in semi-arid areas of Iran using Sentinel-2 satellite images and vegetation indices

Zagros forests of Iran have several environmental, ecological, and socioeconomic values which provide the unique habitats for many endemic species. However, these ecosystems have severely been destroyed by many anthropogenic and natural factors in recent years. Knowledge of area, distribution, and density of these forests and current natural and human-made land uses inside these ecosystems is necessary for protective management of Zagros forests. This research was performed to obtain the land use and forest density maps in Zagros vegetative area of Fars province in southwestern Iran using Sentinel-2A, vegetation indices, Google Earth, and field data. First the boundary of Zagros forests in Fars province was digitized on Google Earth images. Then, Sentinel-2A satellite images covering the forest region in Fars province were obtained from Copernicus website. A pilot area (16000 ha) in the province was considered to investigate the precision of classification methods. Then, Sentinel-2A satellite image of pilot region was classified by some supervised classification methods (ML: maximum likelihood, MD: minimum distance, MaD: mahalanobis distance, SAM: spectral angle mapper, NN: neural network, SVM: support vector machine, and RF: random forest). In addition, the efficiency of different vegetation indices (NDVI, TNDVI, SAVI, and RVI) was evaluated for classifying the forest density in the pilot region. Furthermore, pilot area was considered to test the precision of Google Earth satellite images in this study. For this purpose, 270 field samples were taken as square plots in nine land uses. Selection of the plots inside each land use was randomly. After initial analysis, all satellite images covering the forest area in Fars province were classified by the most accurate algorithm (SVM) to obtain the land use map. On the other hand, the most accurate vegetation index (SAVI) was used to classify the forest density in the study area. Validation of final maps was performed using several random plots on Google Earth images. The initial results of this research indicated that Google Earth images have an overall accuracy (OA) of 96 % compared to ground truth in Fars province. Results of land use map showed that Zagros forest has covered an area of 794651.79 ha in Fars province. The findings of this research demonstrated that SVM algorithm (OA: 92.94 %, and k: 0.85) has efficiently classified the land uses in Fars province. In addition, SAVI (r2 = 0.719, p < 0.001) has highly been correlated to the forest density in Zagros vegetative area of Fars province. Therefore, land use mapping using SVM algorithm and Sentinel-2A images, and forest density mapping using SAVI is highly recommended in Zagros forests of Fars province in time series which is essential for protective management of these forests in spatio-temporal scale.

Eco-Environmental Quality Assessment of Two Seasons Based on Pressure-State-Response Framework by Using Remote Sensing and GIS Techniques

Determining the urbanization and sustainable development rate in Kirkuk City and Taza District (a town located in a suburb of Kirkuk) requires an evaluation of its ecological environment quality (EEQ). This study aims to determine the ecological index (El) and assess it using the popular pressure-state-response (PSR) method based on a set of statistical and remote sensing indicators. The indicators developed by remote sensing data were obtained using Sentinel-2 satellite images to measure the quality of the environment. As new and modern technical assistance for long-term, comprehensive mapping, monitoring through GIS techniques, and the comparison of the Ecological Index in 2023 of the spring and autumn seasons, these indicators provide a basis for decision-making related to sustainable development. It demonstrates the primary relationship between human activity and health under various weather circumstances and contrasts them. It also reveals that the ecological situation is healthier in spring than in autumn (these two seasons are considered transmission periods) due to meteorological conditions, social activities, and economic activity.

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

Integration of geoelectric methods and remote sensing in assessing the environment state in the area of impact of ecologically hazardous objects

Relevance. The need for comprehensive assessment of environmental components in the zones of ecologically hazardous industrial facilities impact, which enables to operatively identify the spatial structure of technogenic pollution. Aim. Chemical pollution assessment of the geological environment in the zones of industrial objects impact, based on the combined use of remote sensing and ground-based geoelectric methods. Objects. Sources of pollution during the production of mineral fertilizers (phosphogypsum dumps, sulfur storage). Methods. The author has developed a complex of methods, consisting of multispectral space imaging (Sentinel-2 satellite images) and ground-based geoelectric researches: water surface resistivity measurements, natural electric field potential imaging, vertical electrical sounding. According to satellite image data (vegetation indices NDVI, NBR, SWVI), technogenic geosystems modifications are distinguished, characterizing different levels of chemical pollution (TM-1, TM-2 and natural geosystem). Within the modification zones, geoelectrical studies are carried out to assess the depth of contamination. Results. In the zone of phosphogypsum dumps impact, two technogenic modifications were distinguished, with a total area of 83.3 hectares, characterized by a statistically significant decrease in the values of vegetation indices compared to the average level. Based on the results of vertical electrical sounding, it was established that the depth of contamination in the TM-1 zone reaches up to 20 m, in the TM-2 zone – up to 2.5 m. The migration of contaminated substances from the dumps occurs with surface drain. In the zone of impact of the sulfur storage, the area of pollution, specified with vegetation indices, covers two hectares. According to the results of the natural electric field method and vertical electrical sounding, the flow of pollution from the sulfur storage site penetrates into groundwater, which is discharged in the TM-1 zone, causing the death of vegetation. In the TM-2 zone, contamination is registered only in the ground aquifer. The developed set of methods enables to quickly and relatively comprehensively diagnose the state of the undergoing technogenic impacts geological environment.

Quantitative analysis of factors influencing damage to old-growth hemiboreal stands as a result of a catastrophic windthrow, based on remote sensing and merged data

The consequences of a catastrophic windthrow in a old-growth hemiboreal stands of the Kologrivsky Forest Reserve were investigated. The degree of damage to tree stands was assessed by interpretation of the Sentinel-2 satellite images. Data from the GBIF portal, SRTM global terrain height models, and tree stand heights were used for the quantitative analysis of factors affecting the presence of wind damage. It was found that tree stands on an area of 277.9 hectares (40.5% of the entire massif) were damaged by windthrow. The results of the analysis of height models and regression models showed that spruce stands are more vulnerable to wind damage, as well as stands of greater height or those growing at higher elevation on the ground.

Análisis espacio-temporal del proceso de urbanización en la ciudad de Santa Tecla, El Salvador

This study aims to analyze the urbanization process in the municipality of Santa Tecla and propose a generic methodology with free tools so that the population can monitor the spatio-temporal process of urbanization in the municipalities of the metropolitan area of San Salvador using Sentinel satellite images and the current regulatory planning instrument as a reference. The evolution of the 30-year temporal space (1991-2021) of the five-year urban sprawl of the municipality of Santa Tecla was analyzed using Landsat satellite data and contrasted with current regulatory instruments or reference techniques. NDVI and NDBI indices calculated from Sentinel-2 satellite data and applying a hybrid image classification methodology, conflicts in real land use were identified with high resolution. The study concluded that the process in the municipality of Santa Tecla has been disorderly in the last 30 years, which has suspected the risk for the population, because although there is a legal framework for land use planning, its application has been flexible. A hybrid method of classification based on NDVI and NDBI from satellite images has been used to simplify the manual inspection of the area in a given period of time with an acceptable margin of error. ILIA: Investigaciones Latinoamericanas en Ingeniería y Arquitectura, No. 01, 2024: 53-60.

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.

Comparative analysis of the performance of selected machine learning algorithms depending on the size of the training sample

Abstract The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. Several variants of each algorithm were tested, adopting different parameters typical for each of them. Each variant was classified multiple (20) times, using training samples of different sizes: from 100 pixels to 200,000 pixels. The tests were conducted independently on 3 Sentinel-2 satellite images, identifying 5 basic land cover classes: built-up areas, soil, forest, water, and low vegetation. Typical metrics were used for the accuracy assessment: Cohen’s kappa coefficient, overall accuracy (for whole images), as well as F-1 score, precision, and recall (for individual classes). The results obtained for different images were consistent and clearly indicated an increase in classification accuracy with the increase in the size of the training sample. They also showed that among the tested algorithms, the XGB algorithm is the most sensitive to the size of the training sample, while the least sensitive is SVM, which achieved relatively good results even when using training samples of the smallest sizes. At the same time, it was pointed out that while in the case of RF and XGB algorithms the differences between the tested variants were slight, the effectiveness of SVM was very much dependent on the gamma parameter – with too high values of this parameter, the model showed a tendency to overfit, which did not allow for satisfactory results.

Seagrass mapping of north-eastern Brazil using Google Earth Engine and Sentinel-2 imagery

Seagrass ecosystems are globally important blue carbon sinks and support significant marine and terrestrial biodiversity. However, human-induced climate change coupled with other anthropogenic pressures have substantially reduced seagrass distributions, making them one of the most threatened ecosystems on Earth. The challenges associated with seagrass conservation include substantial data gaps and limited low-cost, near-real monitoring methods. To address these challenges, we used 507 Sentinel-2 satellite images, filtered between August 2020 and May 2021, in the Google Earth Engine cloud computing environment for regional scale seascape habitat mapping in north-eastern Brazil, a region where conservation efforts are particularly hampered by data limitations. We mapped 9452 km2 of coastline up to a depth of 10 m. We identified 328 km2 of seagrass ecosystems, providing vital open access positional information for a variety of research applications. We also assessed the capability of Sentinel-2 in monitoring temporal changes in coastal habitats, and revealed up to 15.9% declines in seagrass meadow coverage in specific areas over a five-year period in north-eastern Brazil. Our results demonstrate that Sentinel-2 is an effective tool in mapping seagrass distributions at a regional scale. The resulting maps are critical for supporting the conservation of Neotropical coastal biota, including the endangered Antillean Manatee. Our study emphasises the importance of replicable and systematic monitoring methods in the race to conserve threatened coastal ecosystems globally.

Economic valuation of carbon sequestration in Quito’s Metropolitan Park using Sentinel-2 and neural networks

This study introduces an innovative method for assessing the economic value of carbon stored by the urban park forest in Quito, Ecuador. The method uses Sentinel-2 remote sensing data and advanced deep learning techniques. A large forest study area was chosen, and detailed field measurements were taken to gather biomass and carbon data. Sentinel-2 satellite images were processed to correct for radiation and atmospheric conditions, and vegetation indices such as NDVI and EVI were calculated. To model forest biomass, a convolutional neural network (CNN) was created and trained using Sentinel-2 spectral bands and vegetation indices. The model was validated with independent field data and demonstrated high accuracy in estimating biomass and carbon, as indicated by evaluation metrics like RMSE and R². The findings include detailed maps showing the spatial distribution of biomass and carbon in the study area, which can be a valuable tool for forest management and the implementation of policies for valuing stored carbon. This approach combines the high resolution of Sentinel-2 data with the predictive power of neural networks, providing a robust and scalable method for estimating carbon across large forest areas. The study's conclusions emphasize the feasibility and accuracy of this approach and its potential for application in various forestry and geographical contexts.

Geometric Characterization of the Mateur Plain in Northern Tunisia Using Vertical Electrical Sounding and Remote Sensing Techniques

The Mateur aquifer system in Northern Tunisia was examined using data from 19 water boreholes, 69 vertical electrical sounding (VES) stations, and a Sentinel-2 satellite image. Available boreholes and their corresponding logs were compared to define precisely the multi-layer aquifer system, including the Quaternary and Campanian aquifers of the Mateur plain. Quantitative interpretation and qualitative evaluation of VES data were conducted to define the geometry of these reservoirs. These interpretations were enhanced by remote sensing imagery processing, which enabled the identification of the Mateur plain’s superficial lineaments. Based on well log information, the lithological columns show that the Quaternary series in the Ras El Ain region contains a layer of clayey, pebbly, and gravelly limestone. Additionally, in the Oued El Tine area, a clayey lithological unit has been identified as a multi-layer aquifer. The study area, exhibiting apparent resistivity values ranging between 20 and 170 Ohm·m, appears to be rich in groundwater resources. The correlation between the lithological columns and the interpreted VES data, presented as geoelectrical cross-sections, revealed variations in depth (8–106 m), thickness (10 to 55 m), and resistivity (20–98 Ohm·m) of a coarse unit corresponding to the Mateur aquifer. Twenty-three superficial lineaments were extracted from the Sentinel-2 image. Their common superposition indicated that both of them are in a good coincidence; these could be the result of normal faults, creating an aquifer system divided into raised and sunken blocks.