The science and application of the Earth observation system are receiving growing traction and wider application, and the scope is becoming wider and better owing to the availability of the higher resolution of satellite remote sensing products. A water quality monitoring model was developed using Sentinel-2 satellite remote sensing data set to investigate the spatiotemporal dynamics of water quality indicators at Koka Reservoir. L1C images were processed with an Atmospheric correction processor ACOLITE. The months from June 2021 to May 2022 and the years 2017 to 2022 were used for the temporal analyses. Algorithms were developed by using regression analysis and developing empirical models by correlating satellite reflectance data with in situ Chlorophyll-a (Chl-a), turbidity (TU), and Total suspended matter (TSS) measurements. All of the analyzed parameters have determination coefficients (R²) greater than 0.67, indicating that they can be turned into predictive models. R² for the developed algorithms were 0.91, 0.92, and 0.67, indicating that good correlations have been found between field-based and estimated Chl-a, TU, and TSS, respectively. Accordingly, the mean monthly Chl-a, TU, and TSS levels have ranged from (59.69 to 144.25 g/L), (79.67 to 115.39 NTU), and (38.46 to 368.97 mg/L), respectively. The annual mean Chl-a, TU, and TSS vary from (52.86–96.19 µg/L), (71.04–83 NTU), and (36.58–159.26 mg/L), respectively, showing that the reservoir has been continuously polluted over the last seven years. The spatial study found that the distributions of Chl-a, TU, and TSS were heterogeneous, with Chl-a being greater in the south and southwest, and TU and TSS being higher on the western shore of the reservoir. In conclusion, these results show that there are spatial as well as temporal variations on water quality parameters. The proposed algorithms are capable of detecting optically active water quality indicators and can be applied in similar environmental situations.

The world is becoming increasingly urbanized, with migration rates often exceeding the infra-structural capacity in cities across the developing world. As such, many migrants must reside in informal settlements that lack civil and health protection infrastructure, including air quality monitoring. Here, geospatial inverse distance weighting and archived Central Pollution Control Board (CPCB) air quality data for neighboring stations from 2018 to 2021 were used to estimate air conditions in five informal settlements in Delhi, India, spanning the 2020 pandemic lockdown. The results showed that WHO limits for PM_2.5 and NO₂ were exceeded regularly, although air quality improved during the pandemic. Air quality was always better during the monsoon season (44.3 ± 3.47 and 26.9 ± 2.35 μg/m³ for PM_2.5 and NO₂, respectively) and poorest in the post-monsoon season (180 ± 15.5 and 55.2 ± 3.59 μg/m³ for PM_2.5 and NO₂). Differences in air quality among settlements were explained by the proximity to major roads and places of open burning, with NO₂ levels often being greater near roads and PM_2.5 levels being elevated near places with open burning. Field monitoring was performed in 2023 at three settlements and local CPCB stations. Air quality at settlements and their closest station were not significantly different (p < 0.01). However, field data showed that on-site factors within settlements, such as cooking, ad hoc burning, or micro-scale industry, impact air quality on local scales, suggesting health risks are greater in informal settlements because of greater unregulated activity. City-scale models can estimate mean air quality concentrations at unmonitored locations, but caution is needed because such models can miss local exposures that may have the greatest impact on local health.

Vegetation phenology is a primary indicator of ecosystem functioning and climate sensitivity, yet robust monitoring at landscape scales remains challenging in land-use heterogeneous regions such as England. The Copernicus High-Resolution Vegetation Phenology and Productivity product (HR-VPP), derived from Sentinel-2, provides 10 m phenological metrics, including start of season (SOS), end of season (EOS), and length of season (LOS), enabling unprecedented fine-scale mapping of vegetation dynamics.In this contribution, we present a workflow to characterise phenological timing across England using HR-VPP time series from 2017 to 2024. We quantify spatial patterns and interannual variability in key phenometrics and summarise trends across major land cover types.This study provides: (i) an England-scale baseline of phenological timing at 10 m resolution; (ii) an assessment of recent anomalies and climate-driven variability within the HR-VPP record; and (iii) practical guidance for integrating high-resolution Copernicus products into national ecological monitoring. This work supports the broader application of HR-VPP for assessing vegetation resilience to climate and land-use pressures at the national level.