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ABSTRACT Water is the most important substance in nature and all known life forms on the planet depend on it. Among aquatic species, microalgae and cyanobacteria stand out as indicators of their quality, as their increase can be harmful for human consumption, and their concentrations should be monitored. This research aimed to identify reflectance patterns of the microalgae Chlorella vulgaris and Raphidocelis subcapitata that allow, through the creation of an algorithm, the estimation of their chl-a concentrations. The R software found that cell density and growth were higher in C. vulgaris Pearson correlation of 0.96 (p-value 0.000041) and Chl-a content was higher in R. Subcapitata Pearson correlation of 0.81 (p-value 0.013778). An algorithm made it possible to edit the dependent variable based on statistical models. The best readings for the microalgae used were in the spectral range of 524.84 nm in R. subcapitata and 671.13 nm in C. vulgaris. The use of the spectroradiometer for such an analysis, previously unprecedented, was due to the best cost-benefit for measuring the reflectance of Chl-a. It is expected that these two equations will be used in satellites to remotely check water quality through the incidence of these microalgae.