Implementation of remote sensing algorithms to estimate TOC, Chl-a, and TDS in a tropical water body; Sanalona reservoir, Sinaloa, Mexico

The present study implements a methodology to estimate water quality values using statistical tools and remote sensing techniques in a tropical water body Sanalona. Linear regression models developed by Box-Cox transformations and processed data from LANDSAT-8 imagery (bands) were used to estimate TOC, TDS, and Chl-a of the Sanalona reservoir from 2013 to 2020 at five sampling sites measured every 6 months. A band discriminant analysis was carried out to statistically fit and optimize the proposed algorithms. Coefficients of determination beyond 0.9 were obtained for these water quality parameters (r²_TOC = 0.90, r²_TDS = 0.95, and r²_Chl-a = 0.96). A comparison between the estimated and observed water quality was carried out using different data for validation. The validation of the models showed favorable results with R²_TOC = 0.8525, R²_TDS = 0.8172, and R²_Chl-a = 0.9256. The present study implemented, validated, and compared the results obtained by using an ordered and standardized methodology proposed for the estimation of TOC, TDS, and Chl-a values based on water quality parameters measured in the field and using satellite images.