Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic
ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a
machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms
when applied across different bio-optical regimes in inland and coastal waters. The model is trained and vali
dated using a sizeable database of co-located Chla measurements (n=2943) and in situ hyperspectral radio
metric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager
(OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via
two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209mg/m3 and a mean Chla of 21.7mg/m3,
suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error
(MAE) and logarithmic bias (Bias) across the entire range reduced by 40–60%, whereas the root mean squared
logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over
those from the state-of-the-art algorithms. Using independent Chla matchups (n<800) for Sentinel-2A/B and-3A, we show that the MDN model provides most accurate products from recorded images processed via three
different atmospheric correction processors, namely the SeaWiFS Data Analysis System (SeaDAS), POLYMER,
and ACOLITE, though the model is found to be sensitive to uncertainties in remote-sensing reflectance products.
This manuscript serves as a preliminary study on a machine-learning algorithm with potential utility in seamless
construction of Chla data records in inland and coastal waters, i.e., harmonized, comparable products via a
single algorithm for MSI and OLCI data processing. The model performance is anticipated to enhance by im
proving the global representativeness of the training data as well as simultaneous retrievals of multiple optically
active components of the water column.
