LAUSR

Matchup analyses of satellite-derived multispectral remote sensing reflectance (Rrs) products using the ocean color component of the aerosol robotic network (AERONET-OC) is now common practice. Robust matchup analyses are crucial in consistent monitoring of ocean and coastal/inland waters. Differences in the spectral bands of various multispectral satellite sensors and in situ radiometers are one of the sources of uncertainties in matchup analyses. These uncertainties are also present in direct sensor-to-sensor comparisons of Rrs products. To account for the differences in the spectral bands, this manuscript evaluates the utility of deep neural networks (DNN) and compares its performance against other existing methods. A large database of simulated Rrs spectra and a fairly comprehensive hyperspectral in situRrs data set were utilized for training and testing. It was found that the DNN outperforms other existing methods leading to band-average root-mean squared errors of < 4e−4 and < 2.5e−4 1/sr for matchup analyses and sensor-to-sensor Rrs intercomparisons, respectively. These uncertainties are at least ~2X and 3X better than the other methods. The largest uncertainties (i.e., differences in 1/sr) were found when dealing with the green bands over highly turbid and eutrophic waters. The analyses of actual AERONET-OC matchups indicated the need for spectral band adjustments, in particular, in the red channel. It was further revealed that the DNN performance is particularly superior in highly eutrophic and turbid waters. Further research may include exploring other machine-learning techniques or improving the architecture of the neural networks.