An adaptive hybrid algorithm for retrieving particulate organic carbon in complex coastal waters

The retrieval of particulate organic carbon (POC) from optical properties in coastal waters remains a significant challenge because of profound spatiotemporal heterogeneity in water composition. Here, we propose an adaptive hybrid algorithm for POC retrieval (AHA-POC). This algorithm integrates optical water cluster classification with cluster specific model optimization to deliver improved POC estimates in optically complex environments, as demonstrated for the coastal waters of Guangdong and eastern Hainan Island. The algorithm first defines bio-optical water clusters using K-means clustering based on inherent optical properties (IOPs) and derived composite features. This classification separated samples into optically distinct clusters characterized by systematic differences in particle composition, absorption and scattering properties, and bio-optical relationships, which reduce the influence of variability in aquatic particulate matter composition on subsequent POC retrieval algorithms. After cluster definition, cluster specific sensitive features and retrieval models were selected to account for variable POC–IOPs relationships across optical regimes. To avoid station level data leakage, model performance was evaluated using leave-one-station-out cross-validation (LOSO-CV), in which all samples from the same station were held out together during validation. The AHA-POC achieved stable retrieval performance (R 2 = 0.79, RMSE = 0.071 mg /L, MAPE = 32.38 %, Bias = -0.78 %), showing improved accuracy relative to both univariate retrieval models and threshold-based water classification schemes in optically complex water environments. The algorithm was further applied to high resolution in situ IOPs profiles to reconstruct POC sections at 1 m vertical resolution, revealing fine scale vertical heterogeneity associated with riverine plume influence and near bottom particle resuspension. These results demonstrate the potential of cluster specific optical modeling for improving regional POC estimation in optically complex coastal waters and for supporting high resolution assessments of coastal carbon variability.