Proliferation of cyanobacteria is a growing problem in lacustrine environment that results in rapid degradation of water quality. Moreover, certain cyanobacteria species produce harmful toxins. Phycocyanin (PC) is a photosynthetic pigment typical of cyanobacteria and affects the water color: it is therefore possible to study them using remote sensing. At least three algorithms to estimate PC concentration ([PC]) have been published, but their relative errors are important, especially for lower concentration. In this study, we are presenting the results of a new algorithm that uses the second order variability (anomalies) of water's reflectance spectrum to estimate [PC]. This method has never been used in lacustrine environment. The dataset used to develop and validate the algorithm was obtained between 2001 and 2005 in 57 different lakes and reservoirs of the Netherlands and Spain. The performance of the second order algorithm is equivalent or better than the three previously published algorithms. For the subset were [PC] > 32 mg m[superscript -3], the contribution of the second order term (R[superscript 2] =0.68 and RMSE=0.25) seems to improve considerably the first order algorithm (R[superscript 2] =0.50 and RMSE=0.35). The accuracy of the second order algorithm for [PC] > 32 mg m[superscript -3] is superior to the one calculated for the whole dataset (R[superscript 2] =0.69 and RMSE=0.44). The algorithm can also be adapted to the bands of satellite sensor MERIS for the study of cyanobacteria. The application of this algorithm to a MERIS image acquired the 29 August 2010 taken over the Missisquoi Bay (Quebec, Canada) demonstrates the potential of this new algorithm for a future cyanobacteria' monitoring system. Note that all the statistical results presented above are for the logarithm of [PC] and the units of the RMSE are log(mg/m[superscript 3]).