![]() ![]() Ĭillero Castro C, Domínguez Gómez JA, Delgado Martín J, Hinojo Sánchez BA, Cereijo Arango JL, Cheda Tuya FA, Díaz-Varela R (2020) An UAV and satellite multispectral data approach to monitor water quality in small reservoirs. Ĭhang CW, Laird DA, Mausbach MJ, Hurburg CR (2001) Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Ĭaballero I, Stumpf R, Meredith A (2019) Preliminary assessment of turbidity and chlorophyll impact on bathymetry derived from Sentinel-2A and Sentinel-3A satellites in South Florida. īourouhou I, Salmoun F (2021) Sea water quality monitoring using remote sensing techniques: a case study in Tangier-Ksar Sghir coastline. ![]() īartholomew DJ (2010) Analysis and interpretation of multivariate data. ![]() Īrango JG, Nairn RW (2019) Prediction of optical and non-optical water quality parameters in oligotrophic and eutrophic aquatic systems using a small unmanned aerial system. Īpte SC, Batley GE, Szymczak R, Rendell PS, Lee R, Waite TD (1998) Baseline trace metal concentrations in New South Wales coastal waters. Īlvado B, Sòria-Perpinyà X, Vicente E, Delegido J, Urrego P, Ruíz-Verdú A, Soria JM, Moreno J (2021) Estimating organic and inorganic part of suspended solids from sentinel 2 in different inland waters. ![]() Īlparslan E, Aydöner C, Tufekci V, Tüfekci H (2007) Water quality assessment at Ömerli Dam using remote sensing techniques. The developed models were validated with 25% of sample data acquired, and the algorithms showed that multispectral data from Sentinel-2 and RGB data from Unmanned aerial vehicles can be effectively used to estimate the concentration of various water quality parameters with reasonable accuracy in case of large water bodies, including the one chosen for this study.Ībayazid H, El-Adawy A (2019) Assessment of a non-optical water quality property using space-based imagery in Egyptian Coastal Lake. Unmanned aerial vehicle-based Stepwise regression models were employed for assessing Total suspended solids, Total organic carbon and Chemical oxygen demand. Algorithms were developed for assessing the water quality parameters like turbidity, Total suspended solids, Total organic carbon, Chemical oxygen demand, Biological oxygen demand and Dissolved oxygen that were based on Sentinel-2 with high coefficient of determination ( R 2). The correlation between reflectance data obtained from Sentinel-2 and Unmanned aerial vehicle images along with in situ measured data were analysed using stepwise regression method. The water body chosen for this present study is the Ukkadam Lake situated in Coimbatore, Tamilnadu, India (10.9917° North, 76.9722° East). In this paper, a correlation was developed between various optical and non-optical water quality parameters, thereby establishing an indirect relationship between non-optical parameters and reflectance data based on which the predictive models were developed. While most of the studies are restricted in just analysing the optical water quality parameters, only few studies have attempted the estimation of non-optical water quality parameters. Remote sensing was used as a potential solution for monitoring the surface water quality parameters as an alternative to the traditional in situ measurements which are time consuming and labour-intensive. ![]()
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