XGB-2D CNN-Based Dissolved Oxygen Inversion for Coastal Water

Dissolved Oxygen (DO) is an important water quality indicator, reflecting aquatic ecological health and influencing biological and biogeochemical cycles. Remote sensing of DO is exigent yet intricate due to its weak correlation with water optical properties. This study addresses this gap by integrating deep learning (DL) techniques with Chlorophyll-a (Chl-a), temperature, and temporal features. We employed a novel indirect approach to first estimate Chl-a and temperature using in-situ time series water quality data from 94 monitoring stations across the Hong Kong coast, covering five years (2019–2023), Sentinel-2 remote sensing data and normalized cyclic month features using machine learning algorithm eXtreme Gradient Boosting (XGB) and then utilized these predicted Chl-a and temperature values, along with normalized cyclic month features, to estimate DO leveraging DL technique two-dimensional convolutional neural network (2D CNN). XGB model achieved its accuracy in estimation of Chl-a (r = 0.78, RMSE = 3.16 μg/L, MAE = 2.31 μg/L) and temperature (r = 0.96, RMSE = 1.14 °C, MAE = 0.89 °C). 2D CNN yielded a promising DO estimation accuracy (r = 0.78, RMSE = 0.67 mg/L, MAE = 0.52 mg/L). This indirect DO estimation approach achieved 50% greater accuracy than direct methods and also showed 10% improvement in r, 8%–17% reduction in RMSE-MAE values over models trained using Chl-a and temperature only, highlighting the unique potential of XGB-2D CNN approach, incorporating cyclic month features. Inclusion of cyclic month features alongside Chl-a and temperature enabled the model to capture both seasonal variations and long-term trends, significantly enhancing the predictive performance of DO estimations. Consequently, monthly/seasonal mean products of Chl-a, temperature, and DO were generated for spatio-temporal analysis for practical use.