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Title Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3(OLCI) in inland and coastal waters: A machine-learning approach
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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 validated using a sizeable database of co-located Chla measurements (n=2943) and in situ hyperspectral radiometric 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 1209 mg/m3 and a mean Chla of 21.7 mg/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 overthose 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 improving the global representativeness of the training data as well as simultaneous retrievals of multiple optically active components of the water column.

Contributors Nima Pahlevan, Brandon Smith, John F. Schalles, Caren Binding, Zhigang Cao, Ronghua Ma, Krista Alikas, Kersti Kangro, Daniela Gurlin, Nguyen Ha, Bunkei Matsushita, Wesley Moses, Steven Greb, Moritz K. Lehmann, Michael Ondrusek, Natascha Oppelt and Richard Stumpf

Pahlevan, N., Smith, B., Schalles, J.F., Binding, C., Cao, Z., Ma, R., Alikas, K., Kangro, K., Gurlin, D., Ha, N., Matsushita, B., Moses, W., Greb, S., Lehmann, M.K., Ondrusek, M., Oppelt, N. and Stumpf, R. 2020. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3(OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment. 240:111604. (DOI:

Key Words Algorithm development, Cross-site Research, Earth observation, Keywords: Chlorophyll-a, Machine learning, Sentinel missions, Water quality Inland and coastal waters
File Date 2020
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This material is based upon work supported by the National Science Foundation under grants OCE-9982133, OCE-0620959, OCE-1237140 and OCE-1832178. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.