Enhanced Estimate of Chromophoric Dissolved Organic Matter Using Machine Learning Algorithms from Landsat-8 OLI Data in the Pearl River Estuary

Abstract

Chromophoric Dissolved Organic Matter (CDOM) plays a critical role in the carbon and biogeochemical cycles within aquatic ecosystems. Satellite imagery can be employed to determine aquatic CDOM concentrations, highlighting the need for effective and precise algorithms for this task. In this study, a cruise survey dataset containing CDOM absorption coefficients and water-leaving radiances in the Pearl River estuary (PRE) was utilized to develop machine learning algorithms for CDOM retrieval from Landsat-8 Operational Land Imager (OLI) observations. Based on OLI wavelength bands, five bands and six band-ratios were chosen as input parameters for the machine learning models. Six machine learning models were trained to develop CDOM algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The results indicated that, among the six machine learning models, the XGBoost algorithm performed best, with the highest R2 value of 0.9 and the lowest CDOM root mean square error (RMSE) of 0.37 m−1, outperforming empirical algorithms. The XGBoost algorithm identified B4/B1 as the most critical input parameter, contributing 71%, followed by B3/B2 with a 16% contribution, where B1, B2, B3, and B4 are the wavelength bands of the OLI. These two band-ratios accounted for most of the contributions, suggesting their significant role in CDOM retrieval from Landsat OLI images. By employing the developed XGBoost algorithm, CDOM spatial patterns at six instances were derived from Landsat-8 OLI image reflectance, illustrating CDOM variations in the PRE influenced by various factors. Further analysis revealed that, in the PRE, tides and winds are the primary driving forces behind the spatial and temporal variability of CDOM. At present, the exploration of employing machine learning algorithms to infer CDOM concentrations in this region remains relatively limited; therefore, with a higher R2 value, the machine learning model we established unveils fresh and novel results.