Reliable precipitation estimation in mountainous basins is constrained by a sparse gauge network and complex topographic effects. While various open-access gridded precipitation products (GPPs) offer good temporal and spatial coverage, their utility at local and regional scales remains questionable. This may be attributed to relying on single GPPs or uniform models rather than location-specific, integrated approaches. In this study, we have assessed the individual performance of each GPP-model combination and present a Spatially Weighted Grid-Wise Ensemble Learning framework for the Budhi Gandaki basin in the central Himalaya. This study develops an integrated framework that systematically combines nine gridded precipitation products and observations from six rain gauges with four machine learning algorithms using grid-wise evaluation and adaptive station-weighting, producing a merged gridded precipitation product by blending the best-performing GPP-model pair available at each station through spatially weighted integration. Results indicate that some GPP-model pairs, like Asian Precipitation-Highly-Resolved Observational Data Integration Toward Evaluation (APHRODITE), when coupled with Random Forest (Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) equals 5.09 mm/day at station S-1) and XGBoost (RMSE equals 3.94 mm/day at station S-2), showed superior performance in comparison to the other GPPs (RMSE range 8.61-10.14 mm/day). Conversely, satellite, reanalysis, and ensemble-based precipitation products dominate with increasing elevation. The integrated model effectively addresses spatial and performance gaps across individual GPP-model combinations, as evidenced by error propagation analysis at both model and station levels, which yielded a mean RMSE of 4.00 mm/day (ranging from 2.87 to 4.80 mm/day) and a positively skewed error distribution. The merged precipitation product showed colossal improvement in correlation coefficient (CC), rising up to 0.68 in comparison to GPPs, which had CC in the range 0.18 to 0.36. Overall, the study presents a scalable and adaptable framework for enhancing precipitation estimation in complex terrains, with demonstrated robustness across varying elevations and data conditions.

This study explores the use of Machine Learning (ML) to reduce the GPS errors, which are common in dense environments with obstacles like mountains and buildings. The errors observed in GPS positioning are minimized by the application of Machine learning algorithms on the data collected by a GPS receiver mounted on a vehicle. In this study, we applied different machine learning algorithms on the GPS data of the NH48 stretch for 100 km from Pune to Satara to detect and predict future geolocation positioning with respect to speed. This kind of data is generally referred to as spatio-temporal data, as it involves both spatial (latitude and longitude) and temporal (time and speed) components. The 100 km route from Pune to Satara is selected because it influences vehicle movement and GPS performance due to its terrain and traffic density. It provides a realistic and challenging real-world trajectory that helps to evaluate positioning performance under diverse conditions. Out of selected route of total distance of 100 km, approximately 22

Urban air pollution remains a critical environmental challenge in rapidly developing regions, yet there is a limited understanding of spatial pollution susceptibility integrating multiple atmospheric pollutants and land-use dynamics. This study addresses this gap by developing a geospatial framework to assess pollution susceptibility across West Bengal, India, using Sentinel-5P tropospheric monitoring instrument data for key pollutants, including ultraviolet aerosol, carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO). A weight of evidence-based modelling approach was employed to integrate pollutant layers and generate a spatially explicit pollution susceptibility map. The results reveal pronounced spatial variability, with high susceptibility observed in northern districts (Uttar Dinajpur, Dakshin Dinajpur, and Maldah), industrial regions (Paschim Bardhaman and Birbhum), and densely populated urban centres such as Kolkata. Land use land cover analysis between 2017 and 2023 indicates a 44.9% increase in urban and 8.06% decline in vegetation, highlighting urban expansion, a key driver of pollution. This study demonstrates that integrating satellite-based pollutant observations with geospatial modelling provides an effective approach for identifying pollution hotspots and understanding their underlying drivers. These findings offer insights for policymakers and urban planners, enabling targeted interventions such as emission control, sustainable urban planning, and continuous monitoring frameworks for improved air quality management.

Due to the abundance of freshwater outside the polar ice sheets, the Himalayas have been referred to as the "Third Pole" of the planet. Many perennial rivers can be found there, where permanent ice fields and seasonal snow cover melt. A warming climate poses a threat to the glaciers, especially the smaller ones. Due to a lack of long-term records, the dynamic response of medium-sized Himalayan glaciers to recent climate variability is still poorly limited. Using Sentinel-1 SAR velocity mapping, elevation differencing, and other methods, this work provides an eight-year-scale evaluation of glacier surface elevation and velocity variations throughout Himachal Pradesh from 2017 to 2025. With a mean elevation loss rate of −0.32 ± 0.06 m yr−1 and a velocity decrease from 0.15 ± 0.07 m day−1 in 2017 to 0.08 ± 0.02 m day−1 in 2025, the results show a pattern of glacier thinning throughout the area. The substantial relationship between surface lowering and ice-flow slowdown is confirmed by a statistically significant negative correlation (R2 = 0.861) between normalized elevation difference and velocity difference. Results from remote sensing are supported by field observations of Karcha glacier, which show terminal disintegration, debris build up, and gradual standstill. These results show that integrated small and medium-sized glaciers (< 10 km2) are now rapidly losing mass and are in danger. The work emphasizes the need for ongoing satellite-based monitoring to limit glacier response to regional climate forcing and the rapid deterioration of the western Himalayan cryosphere.

This study used hyperspectral imaging (HSI) to analyze various Kolmogorov-Arnold Network (KAN) variants for crop classification in precision agriculture. In order to assess eight different KAN variants, KAN Linear, KALN, FastKAN, ChebyKAN, GRAM, Wav-KAN, JacobiKAN, and BottleNeckGRAM, a standardized KAN-based framework was put into place and tested on the Wuhan UAVborne HSI datasets. Stochastic gradient descent was employed for training, and the experimental setup remained the same for all model variations. JacobiKAN (99.78%) and ChebyKAN (99.99%) had the best classification accuracy of all the tested KAN versions. These results show that polynomial-based KAN models outperform traditional spectral analysis techniques for pattern recognition in hyper-spectral image (HSI) classification. This work provides a comprehensive analysis of KAN variants and lays a strong foundation for future research on KAN-based methods in agricultural remote sensing. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Despite being one of the parts of the global cryosphere that is changing the fastest, Himalayan glaciers’ flow dynamics are still poorly defined because of their rough terrain, enduring cloud cover, and thick debris cover, all of which make it difficult to use conventional optical remote sensing methods. For the glacierized Badrinath area of the central Himalaya, this study creates and applies a multidimensional InSAR framework employing Sentinel-1A/C SAR data to recover the whole glacier velocity field, including Line of sight (LOS), azimuth, and E–N–U directional components. DInSAR and sub-aperture interferometry were used to create LOS and azimuth displacement fields. A geometry-based inversion was then used to get complete directional velocities and a composite multidimensional solution. The findings demonstrate that flow directions that are oblique or orthogonal to the radar line-of-sight cause LOS-only velocities to dramatically underestimate genuine glacier motion, by 60–70% in several trunk and tributary glaciers. The multidimensional velocity field, on the other hand, returns spatially continuous motion channels with more realistic magnitudes (0.07–0.15 m/day), reflecting tiny flow gradients and high-velocity tongues that are suppressed in single-component estimates. Improved accuracy relative to conventional LOS-only approaches is indicated by the statistical analysis (SE = 1.79 × 10⁻5 m/day) and a tight 95% confidence interval (CI) (±3.51 × 10⁻5 m/day) are also demonstrated. A consistent and predictable LOS underestimate is confirmed by Quality Assurance (QA) scoring and Bland–Altman analysis. Strong agreement within 10–15% is demonstrated by validation using field data and earlier Himalayan velocity investigations. The multidimensional InSAR approach offers a strong and physically significant improvement over traditional LOS-based techniques, offering significantly enhanced capability for glacier monitoring, hazard assessment, and comprehension of cryospheric change in high-relief Himalayan environments.

The Siachen Glacier, one of the largest high-altitude glaciers in the Himalayas, plays a critical role in regional hydrology and climate regulation. Continuous monitoring of its snow cover dynamics is essential for understanding glacier behavior and future water-resource availability. This study employs Sentinel-2 Level-2 A imagery from 2022 to 2025 and the Normalized Difference Snow Index (NDSI) to generate monthly snow cover maps and assess spatiotemporal variability across the glacier. Time-series, frequency-domain, anomaly detection, and spatiotemporal consistency analyses were performed to characterize seasonal and interannual snow-cover dynamics. Results reveal a distinct seasonal cycle, with snow cover reaching a maximum of 784.04 km2 during February-March and a minimum of 424.75 km2 in August 2024. Frequency analysis confirmed dominant annual and semi-annual periodicities, while Jaccard Index and Percent Agreement values exceeding 0.91 indicated strong spatial consistency between years. Dynamic behavior ranking showed that 91.64% of the glacier area remained highly stable, whereas only 0.06% exhibited high variability, primarily along glacier margins. Furthermore, a Hybrid Seasonal-Trend Fourier Regression (HSTFR) model was developed to forecast monthly snow cover. The model demonstrated high predictive accuracy (R2 = 0.9298, RMSE = 30.22 km2, MAPE = 2.80%) and successfully reproduced the characteristic annual snow cycle. Overall, the findings highlight the strong seasonal persistence of snow cover over Siachen and demonstrate the potential of the proposed framework for supporting glacier monitoring, water-resource assessment, and climate adaptation planning in high-mountain environments.

Accurate and spatially continuous river bathymetry is essential for hydraulic modelling, habitat assessment, and understanding geomorphic change, but conventional ground surveys are labour-intensive and limited in coverage. This study presents an integrated unmanned aerial vehicle (UAV)-based framework for high-resolution bathymetry retrieval and hydromorphological assessment in a gravel-bed reach of the Bela River (Western Carpathians, Slovakia) for the years 2015 and 2022. UAV-derived red-green-blue (RGB) imagery at 5 cm original resolution was used to extract spectral information for field-measured bathymetric points and develop optical depth-estimation models. To examine the influence of scale on bathymetric prediction, spectral predictors were evaluated within a multi-resolution framework using aggregated grid sizes ranging from 10 to 200 cm. Two spectral feature sets (logarithmic RGB band ratios, additional brightness and saturation descriptors) and four modelling approaches (linear, quadratic, exponential, and Random Forest) were tested, with performance evaluated using root mean square error (RMSE), normalised root mean square error (NRMSE), and coefficient of determination (R2). A Pareto-based multi-objective optimisation was applied to identify the most suitable model configuration by balancing predictive accuracy and spatial resolution. The derived depth maps were subsequently integrated with UAV-based Structure-from-Motion (SfM) terrain models to reconstruct riverbed elevation by subtracting modelled depths from water-surface elevations. The resulting terrain models were used as input for two-dimensional (2D) hydraulic simulations in MIKE+, where Manning's roughness coefficients were calibrated for each year using observed water extents. To ensure consistent comparison, simulations were performed under a common discharge condition, eliminating flow-related variability. Depth and velocity outputs were used to compute the Froude number and classify eight hydromorphological units (slackwater, pool, slow and fast glides, run, riffle, chute, and rapid). Results show improved model stability at intermediate spatial resolutions by reducing radiometric noise. Hydromorphological analysis reveals a shift toward shallower, faster, and more energetic flow conditions, with increased chute habitats and reduced fast-glide units, reflecting channel incision and morphological adjustments. This study demonstrates that integrating UAV-derived bathymetry with hydraulic modelling provides a robust framework for analysing riverbed and habitat dynamics in gravel-bed rivers.

The study of changes in vegetation and water availability is significant for managing land, especially in Bihar, which often faces changing weather. However, earlier studies lacked an in-depth assessment of the variability and interdependency of these two factors. This study fills that gap by assessing the vegetation and water dynamics, as well as their interdependency, over time. Key spectral indices, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), were employed to evaluate vegetation cover and water distribution. The results show a small rise in vegetation (NDVI) with an increase of 0.0044, and a slow drop in water (NDWI) with a decrease of –0.0133. Further, from 2021 to 2024, Bihar’s NDVI ranged from a mean of 0.3751 to 0.4294, with consistently high kurtosis (>5), indicating stable vegetation cover with occasional dense patches. In contrast, NDWI had negative mean values (–0.3709 to –0.4591) and high skewness (up to 2.40) and kurtosis (up to 13.75), reflecting predominantly dry conditions with sporadic water presence. NDVI only showed a significant decline in June (Sen’s slope = –0.01883, p = 0.0415), while NDWI showed moderate but non-significant decreases in March (–0.04132), December (–0.02625), and September (–0.01349). The data also show that both NDVI and NDWI have stayed mostly steady over the years, and there is a strong opposite link between the amount of vegetation and the amount of surface water. A very strong negative correlation (r = –0.9749) was found between average NDVI and NDWI. Also, percentage change showed large changes in both, with NDVI going up by 15.26

This paper presents a fused framework composing three modules for the classification of crops focusing different feature sets and classification techniques. The first module is supervised machine learning model based on Random Under-Sampling (RUSBoost) classification algorithm implemented by taking the input of bio-spectral ensemble features and some specified spectral bands extracted from hyperspectral datasets. The second module is referred as a semi-supervised learning model based on Stacked Autoencoder (SAE) network. SAE is used to extract and classify endmembers, which are pure spectral signatures indicative of different crop types which enhance the network's ability to recognize subtle spectral variations between crops. The third module utilized a Convolutional Neural Network (CNN) that emphasizes the morphological, textural and color based characteristics of the crops. Finally, the outputs of these three modules are combined into a hybrid framework using an ensemble learning approach to produce more robust and accurate classification systems. With the integration of the contribution of each of the bio-spectral ensemble set, specified spectral bands, endmember features, morphological, texture, and color-based features in the hybrid framework, the overall classification accuracy has been substantially enhanced. This fused model have been tested and validated through five crop datasets i.e. Indian Pines (IP), Salinas (SA), WHU_Hi_LongKou (WHLK), WHU_Hi_HanChuan (WHHC) and WHU_Hi_ HongHu (WHHH). For these datasets, the proposed fused model obtained the highest classification accuracies of 96.83

Urban lakes are vital ecological assets that support biodiversity, groundwater recharge, and urban flood regulation but face growing threats from rapid urbanization and the expansion of impervious or “grey” surfaces worldwide. This global trend leads to the degradation of blue (water) and green (vegetation) ecosystems, particularly in developing regions where urban growth outpaces planning and regulation. Despite extensive research on blue–green infrastructure, few studies provide comparative, quantitative assessments of long-term blue-green–grey dynamics across contrasting urban contexts. This study addresses that gap by analysing multi-decadal (1990–2024) Landsat imagery to assess land cover transitions around five lakes—three in Charlotte, USA, and two in Gurgaon, India. It develops novel landscape interaction indicators (Green-to-Blue, Blue-to-Green, Green-to-Grey, and Blue-to-Grey ratios) to quantify ecosystem shifts and forecast ecological tipping points. Results show that Charlotte’s lakes maintain relatively stable blue-green–grey ratios due to effective planning and buffer enforcement, with annual grey surface expansion rates of 1.9–5

Urban NO₂ pollution poses major challenges to public health and environmental sustainability, particularly in rapidly expanding cities like Kanpur. Despite advances in remote sensing, limited attention has been given to the integration of high-resolution multispectral satellite data with advanced machine learning models for fine-scale NO₂ monitoring. This study addresses this gap by integrating Sentinel-2 spectral reflectance and Sentinel-5P NO₂ datasets through a Random Forest regression model to examine the spatial and temporal variability of NO₂ concentrations in Kanpur during 2024. The key objectives include identifying dominant temporal cycles, assessing spectral drivers, and evaluating model performance. Temporal analysis revealed distinct seasonal variation, with peak NO₂ in November and lowest levels in August, influenced by meteorological and anthropogenic factors. The 7th-degree polynomial model effectively captured these seasonal trends with best tradeoff between accuracy and complexity, while frequency-domain analysis detected dominant semi-annual and annual cycles. The Random Forest model achieved optimal performance with 50 trees with R2 = 0.5442 and error of 7.2

Canal networks are vital for irrigated agriculture in semi-arid regions, yet their water quality is increasingly endangered by diffuse agro-chemical runoff and unregulated effluent discharges. Despite this growing risk, long-term, high-resolution assessments that simultaneously capture spatial patterns and seasonal dynamics remain scarce-leaving practitioners with limited evidence for targeted interventions. Addressing this gap, the study sampled ten canal sites monthly for 11 months across Charkhi Dadri District (Haryana, India) and analysed sixteen physicochemical parameters, including heavy metals and irrigation-relevant ions. A suite of multivariate techniques-R- and Q-mode hierarchical clustering, principal-component analysis (PCA), correlation matrices and one-way ANOVA-was employed to disentangle pollution drivers, while the Irrigation Water Quality Index (IWQI) translated complex chemistry into management-ready scores. Two principal components explained 72.6% of variance, with aluminium, iron and copper emerging as dominant contributors; ANOVA revealed significant seasonal shifts (p < 0.05) in these metals. Cluster analysis pinpointed contamination hotspots, and IWQI values of 67.3-85.5 classified canal water as "good" to "very good" for irrigation. By integrating granular spatiotemporal monitoring with advanced multivariate statistics, the study delivers a scalable framework for managing irrigation canals in data-limited, semi-arid landscapes.

Jharia Coalfield is one of the oldest and most crucial mining regions. It faces ongoing challenges with land surface deformation due to mining operations and coal seam fires. Previous studies often overlooked the complex interplay of various LOS and ground velocities and focused mainly on vertical subsidence. This paper used the PS-InSAR for detailed ground deformation analysis of East Jharia. Ascending and descending pass time series LOS deformation datasets were obtained using advanced multi-image sparse point processing. Further, the study employed IDW interpolation in LOS velocity, followed by velocity decomposition to derive horizontal and vertical velocity components. Multi-sparse point processing and IDW interpolation enhance spatial continuity and reduce noise, ensuring the robustness of decomposed velocities. Interdependency and distribution similarity between different velocities were explored using Correlation analysis and the Global Moran's Index. Analysis revealed significant ground movement patterns with weak spatial association and underscored the necessity of both vertical and horizontal velocity for a comprehensive understanding of deformation. Subsidence smaller than -30mm/year was observed in Sahana Pahari, northeast of Rajapur opencast mines, Jharia Main Road, and southeast of Jharia Gurudwara to Kujama Colliery at Tisra. Upliftment greater than 30mm/year occurred in Jorapokhar, Karmik Nagar, and Kustai Basti near Ena Colliery, while lateral displacement of the same value was notable in CIMFR Colony Dhaiya, Koyla Nagar Saraidhela, Ghanoodih, Dobari, Kujama, and Barari Colliery over dumps. Correlation coefficients of 0.9165 (horizontal) and 0.7933 (vertical) revealed the dominant influence of horizontal movement on overall ground deformation. Overall, the study provided valuable insights into the spatial distribution of subsidence for 2023, highlighting the importance of different velocities in assessing and managing ground movement in mining-affected regions.

The systematic biases in the modelled precipitation datasets hinder their applications in various hydrological response studies and lead to ambiguous results. These biases are more pronounced in the complex Himalayan topography, mandating robust bias correction methodologies for the terrain. Previous studies often overlooked correcting the extreme part of the precipitation distribution and comparing bias correction methods. This paper presents a comparative analysis of the six conventional bias correction techniques and a novel ensemble method, EQMX-RF, to correct the satellite and reanalysis precipitation products from 2012 to 2023 for the Budhi Gandaki River Basin (BGRB), Nepal, using daily IMERG V07 Final run (GPM) and ERA5-Land (ERA5-L) precipitation datasets and 6 rain-gauge precipitation data by incorporating 10 statistical parameters both quantitative and categorical, quantile-quantile plots, upper-tail, and seasonal scale analysis. Results demonstrate the good performance of EQMX-RF compared to the conventional bias correction methods in the majority of the evaluation metrics. Using the EQMX-RF method, both GPM and ERA5-Land achieved good skill, with Kling-Gupta efficiency (KGE) values around 0.7, correlation coefficients close to 0.8, very low bias ( 1–2

For the last 5 years, Gurgaon a city in India has been facing an issue of urban flooding due to illicit encroachments over the local waterbodies, poor drainage system and increasing rainfall. In this study, Remote sensing data are employed to find the most flooded areas identified using Partial Least Square Regression and 18 new retention ponds are proposed to build a Sustainable Drainage System (SuDS) in open space and barren lands. In SWMM, the Urban Drainage System (UDS) model is simulated using 24-h rainfall hyetograph from hourly PERSIANN-CSS rainfall data (yearly rainfall events) and 7-h rainfall hyetograph from half-hourly IMERG Global Precipitation Data (extreme rainfall events) from 2000 to 2023. After comparing both UDS and SuDS in SWMM, it is found that the flood volume has decreased significantly from 240 CMS to 180 CMS (for yearly rainfall) and 500 CMS to 350 CMS (7-h rainfall hyetograph). The study also compares the structural resilience of the drainage system under the conditions of no link failure and single link failure scenarios. In no failure situation, 20% more resilience has been achieved for yearly rainfall and 10% more for extreme rainfall events. In single link failure conditions, SuDS is helping to reach 20-47% resilience for yearly rainfall events and 7-30% resilience for extreme rainfall events. Thus, this study helps to achieve SDGs 11 and 13 to build a resilient and climate-adaptive urban drainage in Gurgaon. The study gives significant insights regarding the competency of urban waterbodies to city planners and policymakers.

Sea ice dynamics is considered an important indicator of a changing climate. Sea ice extent rises when the ice approaches the equator. However, the formation of polynyas is a significant process during the thinner ice drifts in the melt season. In this work, an effort is made to utilize radar polarimetry and C-band Sentinel-1 Synthetic Aperture Radar (SAR) data during the melt season (July to August) to map sea ice concentration. Three polarimetric descriptors, namely, randomness, purity, and power, were used to derive the sea ice concentration. A direct relationship was observed between backscatter and span (polarimetric power) values. The degree of polarization (DoP) (polarimetric purity) estimates have advantages over the uniform span values. Polarimetric randomness was useful while segregating small ice fragments in open water areas. For the packed and pancake ice, which had superior clustering of ice crystals, higher concentration rates were observed. In contrast, lower concentration rates were shown for polynyas, thin ice, slush ice, and others. A drastic decrease in concentration levels was seen between 2018 and 2023. The research has potential applications in mapping, calculating, and spatiotemporal estimation of seasonal concentration.

Air pollution is a primary environmental concern mainly in urban areas. Previous research on air pollution in the Konkan region has primarily focused on ground-based measurements, often lacking comprehensive spatial coverage and long-term trend analysis. This study uses satellite data of Sentinel-5P to examine the spatiotemporal trends and hazard zonation of NO2 concentrations in the Konkan region of Maharastra from 2019 to 2023. Various statistical tools such as central tendency and other parameter estimation, along with correspondence analysis were used to assess the variability. The classification was done using the binary mask having a threshold of 0.9*10(-4), and logical operation was employed for hazard zonation. The classification reveals 2,065 sq. km. of the area under the highest hazard level. The analysis also highlighted that the mean NO2 concentration rose from 0.7027 x 10(-4) mole/m(2) in 2019 to 0.7680 x 10(-4) mole/m(2) in 2023, with consistent spatial variability. It may be attributed to persistent emission sources like industrial zones and highways. Spatial correspondence analysis revealed strong associations between consecutive years but a decline over longer periods. However, the overall association value was quite high over the study period. Persistent pollution is observed in areas like Thane, Mumbai, Navi Mumbai, Washi, Dadar and Colaba, along with the highest level of hazard. It reflects the consistency of pollution over a certain area and the need for targeted intervention for pollution control and sustainable environmental development. Overall, this study underscores the significance of satellite-based monitoring for identifying pollution trends and hotspots. It also offers insights into evidence-based mitigation strategies to improve air quality and protect public health.

Air pollution is a primary environmental concern in urban areas. This study examines the temporal and spatial variations in nitrogen dioxide (NO2) concentrations in Kolkata from 2019 to 2023 using Sentinel-5P satellite data. The application of statistical techniques, including Global Moran's I and Fast Fourier Transform (FFT), highlights changes in NO2 spatial distribution and identifies dominant periodicities. Yearly analysis reveals notable fluctuations in NO2 levels, with a significant decline of approximately 9.1% between 2019 and 2020, attributed to reduced vehicular and industrial activities during the COVID-19 lockdown. However, by 2023, NO2 concentrations had returned to pre-pandemic levels attributed to the resumption of economic activities. Spatial analysis reveals higher NO2 concentrations in central built-up areas, including Ballygunge, Bhowanipore, and Park Street. At the same time, peripheral regions such as Metiabruz and Behala show lower levels, likely due to vegetated areas. Global Correspondence values indicate significant shifts in NO2 distribution patterns over the study period. The pattern shifted during the COVID-19 pandemic but stabilized by 2023, aligning with pre-pandemic emission levels, but the monthly pattern was preserved. The FFT analysis reveals a dominant annual cycle with a frequency of 0.0833 cycles per month (12-month period) and an amplitude of 0.3520, along with a significant overall average component. Seasonal variations show higher concentrations in winter due to increased emissions and reduced levels in summer due to photolysis and monsoon rains. These findings underscore the importance of effective pollution management and continuous air quality monitoring to improve air quality in Kolkata.

Glacier surface velocity plays a crucial role in understanding glacier dynamics, climate change impacts, and water resource management. In this study, Differential Synthetic Aperture Radar Interferometry (DInSAR) and geoinformatics techniques were employed to estimate the two-dimensional (2D) surface velocity of glaciers in Zanskar Valley, Ladakh, India. The analysis is based on C-band Sentinel-1 radar data acquired in both ascending and descending orbits to decompose the motion into horizontal and vertical components. The selected glaciers—Pensilungpa, Drang Drung, Khulka, and Kungi—exhibit varying velocity patterns, influenced by topography, ice thickness, and crevasse distribution. The results indicate that the Drang Drung Glacier, the largest in the study area, has the highest surface velocity, reaching approximately - 0.24 ± 0.02 in the upper accumulation zone. Pensilungpa Glacier exhibits distinct velocity variations, with rates of 0.07 ± 0.005 m/day near the equilibrium line altitude (ELA) and lower velocities near the terminus. The vertical and horizontal velocity components provide insights into the dominant glacier flow mechanisms, including ice deformation, sliding, and mass influx from tributaries. The study highlights the effectiveness of DInSAR for estimating glacier motion in complex mountainous terrain. The findings contribute to improved glacier monitoring and future ice thickness assessments, particularly for slow-moving glaciers. The methodology can be extended to other Himalayan glaciers and further refined using multi-frequency SAR data for enhanced accuracy. This research underscores the potential of satellite-based techniques for assessing glacier dynamics and their response to climate change.