Coastal lagoons are highly productive ecosystems that offer a range of natural services for the environment and society. However, anthropic pressure in their basins cause water quality loss and endangers their ecosystem. Monitoring water quality constituent by remote sensing can provide spatial and high temporal resolution analysis. Not recognizing how depth influences on the signal received by a sensor causes on significant errors in estimating water constituent’s concentrations. Using a bathymetry survey and Landsat-5 thematic mapper (TM) images, different patterns of depth influence on reflectance were recognized. By means of a statistical analysis, it was able to classify and analyze three different regions of homogeneous spectral behavior for the closest image from the bathymetric survey and for a median image from 2001 to 2011. Nonparametric Kruskal–Wallis test and Dunn’s post hoc test provided great values in differentiating all groups generated for the coastal lagoon to both closest and median image. The methodology proposed here, being based on statistical methods, can be applied with the use of other images to assist studies in coastal lagoons across the globe.