LAUSR

Abstract The cyanobacterial harmful algal blooms have been observed, over the last decade, in several regions of the southern Caspian Sea, becoming a major threat to human health and aquatic life. The present study aims to develop two models to quantify cyanobacterial concentration in the Caspian Sea using artificial neural networks and multiple band linear regression. The models are based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. Data were collected from the west, center, and east of the southern Caspian Sea between September 2015 and August 2016. The field dataset includes 123 samples in seven different transects and is used to define and evaluate the proposed methods. The Root Mean Square Error (RMSE), unbiased RMSE (URMSE), and correlation coefficient (R) values between Multiple Band Linear Regression Algorithm outputs and field dataset are 1.8 × 10−3 mg.m−3, 22.43%, and 0.73, respectively. For Artificial Neural Network (ANN), the outputs are 1.6 × 10−3 mg.m−3, 18.89%, and 0.81, respectively. The performance of the proposed methods is proven suitable under nearly all conditions of the southern Caspian Sea. However, numerical comparison and visual evaluation of the results show that the ANN method is less sensitive to small changes in the environmental conditions, leading to more stable results. Moreover, the ANN model provides accurate results in most cases, and the accuracy of this results are improved by increasing the training data. This study focused on the development and validation of an optimal algorithm for quantifying temporal and spatial variability phycocyanin concentrations in the Caspian Sea using daily satellite data.