Despite extensive research on chlorophyll-a (Chla) concentration retrieval methods from remote sensing reflectance (Rrs, sr−1) data, there remains a need for more reliable Chla retrieval techniques. In this study, we introduce a deep learning approach based on a 1D convolutional neural network (1D CNN) architecture. In addition, we provide a new method of representing the Rrs as a sequential vector. The model architecture targets the Sentinel-2 MultiSpectral Instrument (MSI) sensor. The proposed model was trained and tested on simulated and in situ data collected from broad trophic states in Japan and Vietnam waters with Chla concentrations ranging from 0.02 to 148.26 mg/m3. The proposed model was evaluated against well-accepted state-of-the-art methods: ocean color three-band (OC3), ocean color index (OCI), two-band ratio, Blend, and a neural network model with a mixture density network. The evaluation shows that the proposed method outperforms other methods with a 7.48–38.02% reduction in root mean squared error (RMSE) and an 11.50–39.17% lower mean absolute error (MAE) than the other methods. The promising performance of the proposed model suggests that more attention should be paid to the domain of sequence modeling for Rrs and the architecture of 1D CNN.