Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chl a ) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chl a . We utilize a database of co-located in situ radiometric and Chl a measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDN A ). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDN A model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDN A model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDN B ) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs ). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDN B models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll- a , coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.

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