Deep Learning Framework for Atmospheric Correction and Chlorophyll-a Estimation from Landsat-8 Images over the Inland Waters of Northern Vietnam

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, plays an important indicator in monitoring trophic states of inland waters. This study proposes a comprehensive framework that utilizes two convolutional neural networks (CNNs) for AC (ConvNet-AC) and Chl-a estimation (ConvNet-CHL) in the eutrophic lakes of Hanoi city (Vietnam) using Landsat-8 images. Satellite-based Chl-a retrieval algorithms have been established based on water remote sensing reflectance (Rrs(λ)). However, existing atmospheric correction (AC) models often struggle to efficiently extract Rrs(λ) due to the complex optical properties of turbid lakes, leading to significant errors in Chl-a retrieval. In this study, a total of 45,764 Rrs(λ) and 13,561 Chl-a samples are synthesized using radiative transfer AC and regional Chl-a retrieval algorithms to address the scarcity of their data. A two-stage training strategy combined with hyperparameter tuning is utilized to automatically optimize the architecture of both networks. Model validation and testing are performed using a subset of synthesized data and an in situ dataset. In the comparative analysis, numerous AC approaches, including atmospheric correction for OLI “lite”, Case-2 Regional Coast Color, Image Correction for Atmospheric Effects, Landsat-8 Surface Reflectance Code, QUick Atmospheric Correction, and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and the existing regional Chl-a retrieval algorithm are implemented. Results indicate that ConvNet-AC achieves an average R2 = 0.72 and RMSE = 0.0024 sr−1 for Rrs(λ) prediction across five spectral bands, outperforming other AC candidates. The ConvNet-CHL achieves R2 = 0.73 and RMSE = 40.40 mg·m−3 for Chl-a estimation within a range between 50 mg·m−3 and 300 mg·m−3, representing a 43% improvement over the existing regional Chl-a retrieval algorithm with RMSE = 71.99 mg·m−3. Furthermore, the proposed framework successfully captures the spatial and seasonal patterns of the Chl-a concentration distributions, demonstrating the effectiveness of integrating CNN-based AC and Chl-a retrieval, offering a robust and transferable solution for monitoring inland water quality with limited ground-truth data.