For the first time, lithological mapping has been carried out for the Shchuchinsky zone of the Polar Urals using the space decoding method on the basis of digital data from the Harmonized Landsat Sentinel-2 remote sensing instrument. The objective of this study is to evaluate the feasibility and effectiveness of Harmonized Landsat Sentinel-2 satellite data with modern image processing methods for lithological mapping of the northeastern slope of the Polar Urals (the Shchuchinsk zone hosting the Yunyaginskoye Au-Fe deposit). We have created a lithological map of the study area showing a strong correlation with the existing geological map of the region. Specifically, metamorphosed rocks are reliably identified by dark blue to violet hues, ultramafic rocks by red, and a combination of green and blue indicates areas of sedimentary and metamorphic rocks. The study results confirm the effectiveness of the chosen approach and demonstrate a significant potential of using this satellite imagery for geological mapping in high-mountain terrain with a thin cover of Quaternary sediments (up to 6 m). To further improve the accuracy and expand the method applicability, integration with other satellite image processing techniques that effectively identify lithological units under thicker Quaternary cover is recommended together with fieldwork verification.

The article is devoted to the assessment of the accuracy of the ArcticDEM digital elevation model in comparison with airborne laser scanning (ALS) data on the Kola Peninsula. ArcticDEM, created on the basis of stereoscopic processing of satellite images, has a high level of detail (resolution up to 2 m), but its accuracy requires verification, especially in conditions of complex relief and vegetation cover. As a standard, the authors used airborne laser scanning data with a density of more than 6 points per m2, covering an area of 260 km2. The main results of the study show the presence of systematic deviations of several decimeters, which may require correction to improve the accuracy of the data. ArcticDEM data have good filtering of vegetation cover, without which the final values may have an order of magnitude greater deviations. Errors increase with increasing relief dissection. ArcticDEM meets the requirements for creating topographic plans at a scale of 1:10 000 with a relief section of 5 m, but is not suitable for a scale of 1:5 000 due to insufficient accuracy. Significant deviations (up to 1 m) are observed on the water surface on water bodies, which is due to the limitations of stereophotogrammetry for homogeneous textures. Thus, ArcticDEM demonstrates high accuracy, including in tundra and forest-tundra conditions, but its use requires taking into account systematic errors and assessing the ruggedness of the relief. The model is suitable for regional studies, but in mountainous areas its accuracy can drop significantly. The research results confirm the errors declared by the manufacturer if their values are interpreted as the value of one standard deviation. The study emphasizes the importance of validating global DEMs with local high-precision data, such as ALS, to ensure the reliability of results in scientific and applied problems.

This research develops an integrated methodology to evaluate groundwater recharge potential in arid and semi-arid ecosystems by combining remote sensing, geographic information systems (GIS) and multi-criteria analysis. Five geographic regions (Kenya, India, Botswana, Ethiopia and Saudi Arabia) were analyzed through comparative assessment, identifying variability in very high recharge zones ranging between 0.07% and 14%. The methodology applies high-resolution satellite image processing, geospatial modeling and Analytical Hierarchy Process (AHP) to characterize subsurface water systems comprehensively. The results demonstrate methodological precision between 60.53% and 87.9%, with Areas Under the Curve (AUC) ranging from 0.604 to 0.879, indicating robust model performance across different hydrogeological contexts. The precipitation regimes analyzed vary from 73.8 to 1500mm annually, confirming that recharge potential depends on complex interactions between geomorphological, climatic and structural factors. Statistical validation through ROC curve analysis and empirical well data verification supports the reliability of the integrated approach. The study establishes the need for adaptive methodologies that recognize the dynamic nature of groundwater recharge systems, contributing to sustainable water resource management strategies in water-scarce regions.

Satellite image denoising is essential for preserving image quality in remote sensing applications, where impulse noise significantly degrades captured data. To address this challenge, this method proposes an ultra-fast parallelized modified decision-based median filter (PMDBMF). It effectively removes impulse noise while preserving structural details. The proposed approach leverages fixed parallelization to achieve superior noise reduction with minimal computational overhead. Compared to the decision-based median filter (DBMF), the proposed PMDBMF approach achieves an overall improvement of approximately 13 %. This result demonstrates the efficiency of PMDBMF in delivering high-quality noise removal while significantly reducing processing time, making it a promising solution for real-time satellite image processing. Additionally, the PMDBMF maintains fine image details while effectively suppressing impulse noise, ensuring superior structural integrity compared to traditional median-based approaches. Its fixed parallelization strategy enhances scalability across various hardware architectures, enabling real-time deployment in resource-constrained environments. This efficiency is highly significant for research-driven domains such as environmental monitoring, disaster assessment, and geospatial analysis, where rapid and reliable image restoration is essential. Experimental analysis confirmed that the proposed PMDBMF framework achieves superior structural integrity, with robust edge and texture preservation, and enhanced noise suppression, as evidenced by notable improvements in the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index metrics (SSIM), and computational complexity metrics.

Satellite image processing in the circumpolar north: Understanding climate crisis by predicting sea ice extent in the arctic

Temporal and spatial pattern analysis and forecasting of methane: Satellite image processing

This study aims to develop a strategy for optimizing the selection of geophysical sites for HVA drilling in the bedrock area of Kong. To achieve this objective, the methodology first consisted of extracting the fracture network from satellite image processing. Then, surveys and electrical drags were used to characterize the geometry of the fractured aquifers (the depths of the aquifer zones and the fracturing indices of the conductive anomalies). The conductive anomalies K, H, U, and V were identified by electrical drags and intersected the fractures determined by processing Landsat 7 ETM+ satellite images. In addition, the fracturing index ranges from 1.96 to 2.57. The corrected cumulative lengths of the fractures range from 362.92 m to 835.46 m. Interpretation of the borehole curves showed 4 to 5 aquifer zones in the subsoil. Finally, combining the fracturing index, the depth of the aquifer zones, and the length of the fractures resulted in the following drilling priority order: K (F1), U (F2), V (F3), and H (F4) (K > U > V > H).

Purpose. To develop a novel approach for fusion of optical satellite images based on machine learning and quantum optimization for integrating spatial-spectral information from RGB and IR channels. Methodology. The proposed approach involves sequential processing of input data, including geometric, radiometric, and atmospheric corrections. Each channel is decomposed into low-frequency and high-frequency components using a Gaussian filter. The Independent Component Analysis (ICA) method is applied to reduce the dimensionality of input data. A quantum optimizer approximation algorithm is applied to analyze the infrared channel. A deep convolutional neural network with residual dense blocks is used to extract spatial structural features from RGB channels. After integrating features through fully connected layers, the quantum block optimizes the weight coefficients for the final channel fusion. Findings. Quantitative evaluation demonstrates that the proposed approach outperforms classical fusion methods, including Brovey, Gram-Schmidt, IHS, HCS, HPFC, ATWT, PCA, and CNN, in spectral and spatial information integration accuracy. The method achieves the lowest mean squared error (MSE = 191.8), high structural similarity index (SSIM = 0.43), entropy (Entropy = 7.54), and Sobel filter range (Sobel Sharp = 19.19–21.67 across R, G, B channels). Visual analysis also confirms qualitative advantages: images exhibit clear structure without artifacts and balanced color reproduction consistent with the spectral characteristics of the original RGB data. Originality. A novel approach to utilizing information of the IR channel is proposed, which integrates a quantum-classical algorithm within a deep convolutional neural network architecture for synergistic processing of multichannel optical images using multilevel frequency decomposition and a weighted feature fusion mechanism. Practical value. The proposed approach can be implemented in Earth remote sensing systems to enhance the quality of satellite image processing, particularly for mapping, land resource monitoring, agricultural control, and environmental analysis tasks. Applying quantum algorithms opens new opportunities for improving efficiency and accuracy in processing multidimensional geoinformation data containing IR channel information.

Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management.

This study aims to improve the knowledge of farmers and pastoralists on the management of spatial conflicts through updated information on the dynamics of agriculture-grazing. To achieve this objective, the methodological approach consisted of collecting and processing Landsat satellite images TM, TM+/OLI-TIRS to highlight the dynamics of land cover from 1998 to 2024 in the Bouandougou Sub-Prefecture, on the other hand a socio-economic survey was conducted among the various stakeholders. We obtained performance indicators that are higher than recommended (80% overall accuracy and 75% Kappa coefficient) in the processing of satellite images. The analysis of the dynamics shows that the mosaic shrub savannas annual crops increased by 52.82% from 1998 to 2024, forest islands lost 41.96% of their areas over the same period. In order to promote peaceful coexistence between farmers and livestock breeders in agro-pastoral regions, the integration of geographic information system tools should be considered to strengthen policy decisions in the management of agro-pastoral conflicts.

ABSTRACTStudying pre‐Hispanic agricultural landscapes requires large‐scale spatial analysis and detailed slope assessment to understand water management systems. Remote sensing and photo‐interpretation techniques are effective for investigating historical irrigation structures. This research developed a high‐precision planimetric model by integrating satellite imagery, digital elevation models (DEMs), slope analysis and hillshade visualisation. It focused on the pre‐Hispanic agricultural site in the lower Miriguaca Basin (Antofagasta de la Sierra, Argentina), specifically Red Miriguaca 1 (RM1). Four methodological approaches were applied: (1) acquisition and processing of free satellite images and DEMs using QGIS; (2) generation of planimetric data, with integration of ground control points to enhance accuracy; (3) visual interpretation of hydraulic structures in the planimetric model; and (4) cross‐checking diverse data (archaeological GPS control points and pre‐existing hydraulic model) to corroborate hydraulic mapping. The model allowed identification of new sections of the main canal, secondary canals and the water intake point at RM1, clarifying the topography‐driven hydraulic design. It also revealed connections between RM1 and other irrigation networks previously considered independent. This approach provides a cost‐effective tool for reconstructing ancient water management in arid regions, with potential applications in similar archaeological contexts.

This study explores whether the rural area of El Salvador can be subdivided into groups of municipalities where each group has its own characteristics in terms of variables GDP per capita, electricity consumption per capita, population density, poverty rate and night light. The study was performed horizontally considering the spatial distribution of light. The light was obtained by processing satellite images with Geographic Information Systems (GIS) software. Based on the nature of the data, it was decided to apply advanced statistical clustering techniques that, supported by the advantages of computing, would allow comparing 1000 cluster possibilities by changing the classification parameters such as the method and the distance used. The study concludes that at the exploratory level, subdivision with hierarchical cluster technique is possible only by incorporating night light and advanced techniques with t-SNE. It was found that the best model of subterritories is grouped into nine categories where two groups are mainly municipalities with urban predominance and the rest with rural predominance.

The Batutegi Reservoir is one of the vital infrastructures in Lampung Province, serving as a source of irrigation water, hydroelectric power, raw water, and flood control for the surrounding area. However, changes in vegetation density in the catchment area can affect the rate of erosion and sedimentation in the reservoir, which impacts the storage capacity and useful life of the Batutegi Reservoir. This study aims to analyse changes in vegetation density in the reservoir catchment area using Sentinel-2A imagery and to assess its impact on sedimentation and total suspended solids (TSS) levels in Batutegi Reservoir during the period 2015-2024. The methods used included NDVI and WRI image analysis with satellite image processing using SNAP and ArcMap software, as well as SWAT model simulation to predict erosion and sedimentation. The results showed a 37.41% decrease in high-density vegetation area and a 339.32% increase in low-density vegetation during the observation period. The highest TSS level was recorded in 2018 at 14.51 mg/l, in line with high rainfall due to the La Niña phenomenon. SWAT simulations indicate the highest increase in sedimentation load in the same year, reaching 2,587.90 tonnes/year, with the largest contribution coming from sub-catchment area number 71 and 75. The correlation between sedimentation values from SWAT and Sentinel-2A showed a determination coefficient (R²) of 0.917. This study confirms that a significant decrease in vegetation density exacerbates reservoir sedimentation. Regular monitoring of changes in vegetation density and reservoir water quality, particularly TSS values, is an important strategy for maintaining the useful life and capacity of the Batutegi Reservoir

On October 29, 2024, a cut-off low (DANA) caused the most catastrophic flooding in recent history in Spain and the Mediterranean region, in Valencia, resulting in 228 deaths, more than €13 billion in damages, and the disabling of more than 140,000 vehicles. In the days following the disaster, a lack of information and a limited institutional response created a climate of uncertainty. In this context, satellite imagery became the only reliable source of information. This study adopts a systematic review methodology to reconstruct and critically analyze how geospatial technologies were used for forecasting, documenting, and managing the disaster. It draws on a compilation of meteorological datasets, satellite imagery (e.g., Sentinel, Landsat), GIS outputs, institutional maps, and academic research. The research identifies four chronological phases: First, meteorological data were employed to sound the alarm; second, satellite imagery products were used when the dis-aster already occurred; third, development of web platforms with geographic information and other institutional servers for data download; and four, new lines of research with the inputs generated in the previous points. The intervention of international coordination platforms—the Copernicus EMS rapid mapping service and the International Charter: Space and Major Disasters—allowed, in record time, the processing of the first satellite images and the expedited mapping of flooded areas. The findings demonstrate that spatial analysis tools are one of the most important inputs when dealing with a natural disaster, especially in the first hours and days following the event. However, prior territorial planning and the prompt intervention of decision-makers when such an event occurs are the most decisive factors in minimizing damage. The study al-so contrasts climate change-based explanations with historical-geographic interpretations of the disaster, underscoring the need for a comprehensive, geographically grounded approach to future risk management.

Satellite imagery pre-processing and feature extraction for the mapping of coastal ecosystems using Google Earth Engine: A workflow for practitioners

Satellite images have been and continue to be a major area of study due to their significant contribution to the monitoring and assessment of environmental conditions using numerous indices of different categories (e.g., vegetation, water). However, processing large-scale, multitemporal datasets remains time-consuming and technically complex. To address this, an open-source QGIS Python-based plugin named Remote Sensing Toolkit (RST) was developed to automate the processing of satellite images and the computation of 100 biophysical indices from five satellite missions (i.e., AVHRR, MODIS, Landsat-8/9, Sentinel-2, ASTER). RST includes key preprocessing tools such as cloud masking, scaling, and clipping by mask layers, in addition to an AI-based outlier detection module that is integrated to enhance result reliability, all with a programming-free interface and customizable parameters. The plugin's utility was demonstrated through an application to the 2024 Spain flash flood using Sentinel-2 data from 2022 to 2024. A SARIMA model was used to detect temporal anomalies in NDVI-derived water extent time series, revealing a significant deviation coinciding with the flood event. Moreover, spatial maps of flood extent were generated to visualize and quantify the affected areas, and a comparative assessment with Copernicus Emergency Management Service Rapid Mapping (CEMS RM) products was conducted to evaluate the accuracy of detected flood extents. This analysis highlighted the complementary value of NDVI-derived flood maps and demonstrated the plugin's effectiveness in automating satellite data workflows for environmental monitoring and rapid disaster assessment.

Pati regency is a coastal area that contributes significantly to Indonesia’s fisheries sector. One if its major fishing ports, PPP Bajomulyo, serves as a hub for fishing activities, with catches ranging from small to large pelagic fish, including Spanish mackerel (Scomberomorus commerson). This study aims to examine to relationship between oceanographic parameters and Spanish mackerel catch volumes at PPP Bajomulyo over the 2021-2023 period. A survey-based approach incorporating satellite image overlay was employed. The analysis of non-linier relationships between variables was conducted using Generalized Additive Model (GAM), implemented in RStudio. Satellite image processing was performed using SeaDAS, while spatial analysis was carried out in ArcGIS. The findings reveal that the interaction between sea surface temperature (SST) and chlorophyll-a concentrations accounted for 11,44% oof catch variability. Elevated SST above 29,5 °C were associated with increased catch rates, whereas extreme temperature (>35 °C or <28 °C) corresponded with significant declines. Chlorophyll-a exerted a relativity weak influence on catch, suggesting that although it serves as a proxy for primary productivity, Spanish mackerel distribution is more strongly governed by thermal conditions and prey availability. The study further identified that optimal fishing grounds were predominantly located in northern Central Java waters, particularly during seasons characterized by favorable oceanographic conditions.

Noctilucent clouds, which form during the summer months primarily over the polar regions, are also fre quently observed at temperate latitudes. These regions play a key role in shaping the total area and spatial configuration of mesospheric cloud fields. As shown in previous studies, the seasonal and interseasonal evolution of these fields is largely influenced by meteorological processes in the mesosphere, though the role of geophysical fields is also considered. This work describes the development of a methodology that enables a shift from studying the integral characteristics of noctilucent clouds to analyzing their differential properties. At the initial stage, the goal is to investigate the presence of longitudinal structure by dividing satellite images of noctilucent cloud fields into 30-degree longitudinal sectors and calculating the cloud area within each. Achieving this requires the creation of specialized software, and a significant portion of the study is devoted to describing the algorithm design, programming language selection, and implementation process. The performance of the new software is compared with existing approaches, demonstrating that the developed method provides substantially improved accuracy in detecting longitudinal inhomogeneities in the global distribution of noctilucent clouds. Key words: noctilucent clouds, digital images, longitudinal cloud structure, hydrometeorology, hydrocli matology, cloud physics and chemistry.

ABSTRACT The advances in Machine Learning (ML) and computer technologies enabled to process satellite images using programming. Environmental applications that handle Remote Sensing (RS) data for spatial analysis use such an approach, for example, Python’s library scikit-learn using algorithms on pattern identification, predictions or image classification. This paper presents an ML method of satellite image processing using Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). The aim is to classify multispectral Landsat images using ML for identification of changes in salt pans of West Mauritania, Africa over the period 2014–2023. We define 10 classes of land cover categories and perform analysis of geological, lithological and landscape setting, and then introduce the principles, algorithms and processing of the ML methods of GRASS GIS. The following classification models were employed to implement image classification with training: Random Forest (RF), Decision Tree, Gradient Boosting and Support Vector Machine (SVM). The results were compared with clustering performed by k-means and maximum likelihood discriminant analysis. The cartographic visualisation and validation was implemented through accuracy analysis. Results for the best performing SVM model with seven-band input produced an overall accuracy of 76%, for the RF model – 73%, compared to 69% for Decision Tree Classifier – 69% and for Gradient Boosting Classifier – 67%. The SVM model embedded in GRASS GIS generates robust land cover maps with good accuracy from multispectral satellite images. The paper demonstrated an ML-based automated approach to satellite image processing, which links Artificial Intelligence (AI) with cartographic tasks.

Satellite Images Processing for Monitoring Lake Surface Area