 This study aimed to analyze algal phenomena quantitatively and qualitatively using synoptic monitoring, algal pigment analysis, and a deep learning model. Water surface reflectance was measured using field monitoring and drone hyperspectral image sensing. The algal experiment conducted on the water samples provided data on major and minor pigments including chlorophyll A, phycosyanin, lutein, fucosanthin, and zexanthin. Based on the reflectance and absorption coefficient spectral inputs, a one-dimensional convolutional neural network, 1D, CNN, was developed to estimate the concentrations of the major and minor pigments. The 1DR-CNN could model periodic trends of chlorophyll A, phycosyanin, lutein, fucosanthin, and zexanthin compared to the observed ones, with our two values of 0.87, 0.71, 0.76, 0.78, and 0.74, respectively. In addition, major and secondary pigment maps developed by applying the trained 1D, CNN model to the processed drone hyperspectral image input successfully provided spatial information regarding the spots of interest. The model provided explicit algal biomass information using the estimated major pigments, and implicit taxonomical information using accessory pigments such as green algae, diatoms, and cyanobacteria. Therefore, this study provides strong evidence of the extendability of deep learning models for analyzing various algal pigments to gain a better understanding of algal blooms. This article was authored by Zhongqiu Qiu, Sik Minit Hong, Ji Zhang, and others. We are article.tv, links in the description below.