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

Abstract

Abstract 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 metadata from LANDSAT-8 imagery (bands) were used to estimate TOC, TDS, and Chl-a of the Sanalona reservoir from 2013 to 2020 at 5 sampling sites measured every six 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 (r2TOC = 0.90, r2TDS = 0.95 and r2Chl − a = 0.96). The validation of these proposed models was carried out by comparing the estimated values of TOC, TDS, and Chl-a with the historical database provided by CONAGUA. 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.