This research demonstrated the feasibility of applying Sentinel-2 images to generate empirical models and estimate physicochemical parameters concentration, particularly nutrients in the wetland system called Bajo Sinú wetlands complex, Colombia. Spearman correlations were determined between water quality parameters, which were monitored at 17 points in the wetland on 5 February 2021, with Sentinel-2 images reflectance values from the same monitoring date; the correlations allowed the identification of statistically significant bands in the multiple linear regression algorithm implementation to determine empirical water quality models. The results show significant correlations between the optically active parameters, TSS-Turbidity, which in turn correlated with the optically inactive parameters Turbidity-NO3 and TSS-DO, as well as non-optically active parameters among themselves, TDS-NO3 and TDS-TP; the empirical models presented higher than 74.5% fit (R2), particularly DO (R2 = 0.948), NO3 (R2 = 0.858) and TP (R2 = 0.779) were the models with the highest fits (R2). These models allowed us to properly estimate the spatial distribution of nutrient-forming compounds in the wetlands complex. The determinant role played by turbidity in this type of water body is highlighted; it acts as a connecting constituent that makes the estimation of water quality parameters without spectral response through remote sensing feasible. Sentinel-2 images and multiple linear regression algorithms have been shown to be effective in estimating the concentration of water quality parameters without spectral response, such as NO3 and TP in shallow tropical wetlands, due to the processes of transformation, interaction and dependence between the different environmental variables in aquatic ecosystems.
This study aims to implement remote sensing to determine water quality parameters. Based on the water quality parameters measurements on February 5, 2021, and the LANDSAT 8 satellite images reflectance values, statistical models were generated by the RLM method: Stepwise Regression in Matlab software and digital models through QGIS and ArcGIS Pro GIS. The models obtained for pH, temperature, and turbidity were above 0.6 R2, while dissolved oxygen was above 0.8, showing a good correlation between in situ and estimated data. It is important to avoid cloudy conditions covering the study area because they limit the mathematical models’ application, altering or preventing the results from being generated. The implementation results of this type of technology are of great relevance for future projects, where water quality assessments can be made by using national and international regulations according to the use either for human consumption or for aquatic life protection.
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