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@article{191977,
        author = {Balaji Bhausaheb Yadav and Digambar Ashokrao Solanke and Vikas Vilas Ghodke and Rohit Angadrao Kargude and Sandipan S. Sawant and Shafiyoddin Sayyad},
        title = {Spatiotemporal Assessment of Inland Reservoir Water Quality Using Sentinel-2 Imagery and a Quasi-Analytical Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8722-8744},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191977},
        abstract = {Degradation of water quality in inland reservoirs presents a major challenge for sustainable water resource management, particularly in data-scarce regions of developing countries. This study provides a spatiotemporal assessment of water quality in Majalgaon Dam, Maharashtra, India, using multispectral Sentinel-2 imagery integrated with a Quasi-Analytical Algorithm (QAA). Satellite observations were employed to derive spatially distributed estimates of total suspended solids (TSS) and chlorophyll-a (Chl-a) based on bio-optical modeling, while the Normalized Difference Water Index (NDWI) was used to accurately delineate the reservoir extent. To demonstrate calibration and validation workflows in the absence of concurrent field campaigns, a synthetic dataset comprising 50 stratified sampling locations was generated under realistic measurement assumptions. The analysis revealed strong spatial heterogeneity, with elevated TSS near tributary inflows, moderate concentrations in shallow near-shore zones, and consistently lower levels in the central basin. Chlorophyll-a exhibited a partially decoupled spatial pattern, with localized biomass enhancement in sheltered embayments. Seasonal analysis for 2023 indicated pronounced monsoon-driven dynamics, characterized by increased TSS during monsoon months and higher Chl-a during post-monsoon periods. Multi-year analysis (2018–2023) showed substantial inter-annual variability linked to climatic and watershed controls. While linear calibration using synthetic data showed limitations in absolute concentration accuracy, satellite-derived products effectively preserved relative spatial and temporal patterns. The results highlight the utility of Sentinel-2–based remote sensing as a scalable and cost-effective approach for reservoir water quality monitoring and management.},
        keywords = {Water quality monitoring, Sentinel-2 multispectral imagery, Quasi-Analytical Algorithm, Total suspended solids, Chlorophyll-a, Remote sensing, Bio-optical modelling, Spatiotemporal variability, Inland reservoirs, Reservoir management.},
        month = {February},
        }