This study presents a novel deep learning-based superresolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. We developed a convolutional neural network architecture that upscales low-resolution satellite imagery to generate high-resolution (0.5m) outputs, achieving a peak signal-to-noise ratio (PSNR) of 32.4 dB and structural similarity index (SSIM) of 0.91. The enhanced imagery enabled precise delineation of urban features and environmental parameters affecting groundwater quality. Using the super-resolved images alongside traditional water quality parameters (pH, hardness, TDS) analyzed through Fuzzy AHP, we calculated the Groundwater Quality Index (GWQI) for 33 areas across four years (2008-2020). Results showed most areas achieved “Better water” quality status by 2020, though two regions (Old City and Anarkali) were classified as “Poor water” quality. We observed a moderate negative correlation (r = -0.62, p < 0.001) between GWQI and static water level depth, with significant depth increases in areas like Dholanwal (37.1m), Ichhra (47.06m), and Township (49.35m) by 2020. The integration of super-resolution remote sensing with conventional water quality assessment demonstrates promising applications for urban environmental monitoring and groundwater resource management.

This study advances multi-sensor remote sensing data fusion integrating optical (Sentinel-2, MODIS), thermal (LST), and hydrological (SMAP) sensors with climate datasets to evaluate soil moisture dynamics at five depths (0–50 cm) across nine agricultural zones (October 2021–September 2023), leveraging AI and machine learning to address data quality challenges in heterogeneous sensor inputs. Our novel AI-driven approach improved data fusion accuracy by 27% compared to conventional methods, enabling more reliable drought detection even in areas with persistent cloud cover and sensor inconsistencies. Using Google Earth Engine for spatiotemporal fusion and Random Forest classification for feature optimization, we demonstrate the superiority of fused multi-sensor indices, with VCI (Vegetation Condition Index) and NVSWI (Normalized Vegetation-Soil Water Index) achieving the highest accuracy (r = 0.56–0.59) at 10–40 cm depths, while single-sensor optical indices underperformed at 50 cm (r < 0.30). Temporal analysis revealed cumulative drought signals in SPEI (Standardized Precipitation Evapotranspiration Index) (3–6-month lag) and rapid surface responses in TCI (Temperature Condition Index), emphasizing the need for adaptive sensor fusion. Land cover changes included an 18.5% reduction in surface water and a 3,714.6 km² expansion of bare ground in 2023, correlating with SPI (Standardized Precipitation Index)-based drought severity (29.8% to 47.3% affected area). The framework's AI-driven error correction and multi-sensor synergy provide a scalable model for coastal applications, such as mangrove ecosystem resilience monitoring (integrating thermal, optical, and radar data for salinity intrusion analysis) and hydrological forecasting (fusing soil moisture, precipitation, and tidal datasets). By resolving sensor inconsistencies and enhancing reliability in complex environments, this work underscores the broader relevance of multi-sensor fusion for coastal vulnerability assessments, where land-sea interactions demand integrated sensor networks and machine learning to mitigate ecological and climatic risks.