Physical-chemical Characterization of Surface Water Bodies by Means of Remote Sensors: Case Study Dam j.a. Alzate, Mexico

Water characterization involves the estimation of water quality parameters (WQP) such as total suspended solids (TSS), total nitrogen (TN), chemical oxygen demand (COD) and total phosphorus (TP). The estimation of these WQP is usually carried out through laboratory processes, which may require considerable time and costs. An alternative tool for water analysis are remote sensors that allows the correlation between water quality data and the reflectance of surface water bodies. This study proposes the use of the Landsat 8 Oli image bands in order to estimate four WQP and validate them by means of fields samples. The importance of estimating WQPs with remote sensors lies in the its results accuracy, while requiring less time and costs for analysis in comparison with traditional methods. To obtain the WQP functions based on water reflectance, several multivariate regressions were proposed, such as linear, exponential and polynomial regressions. This analysis is applied to the J.A. Alzate, Dam. Mexico as case study due to the concentrations of pollutants carried by the water coming from the Toluca Metropolitan Area (TMA). The analysis considered 14 field samples, 7 of which were recollected during dry season (05/19/2018) and 7 during rainy season (10/16/2018)

The methodology of this study has three phases, pre-processing (boundary of the study area, calibration and atmospheric correction), processing (sample size, multiple regression and validation) and post-processing (extrapolation). Results show that the linear model regression provides the highest determination coefficient for NT and TSS, and linear polynomic regression for PT and COD. The method requires field samplings on different dates (before and after rain season) to design the regression model; the use of the MODTRAN 4 model for atmospheric correction of the study area; sensitivity treatment for each satellite band for every WQP; as well as the use of the band-ratio technique for the polynomial regression model

Keys word: remote sensors, multivariate regression, reflectance, J.A. Alzate Dam. Landsat 8 Oli.