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Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematicfor relatively turbid coastal waters due to the impact of non-algal materialson atmospheric correction and standard Chl algorithm performance. Artificialneural networks (NNs) provide an alternative approach for retrieval of Chl fromspace and results in northwest European shelf seas over the 2002-2020 periodare shown. The NNs operate on 15 MODIS-Aqua visible and infrared bands and aretested using bottom of atmosphere (BOA), top of atmosphere (TOA) and Rayleighcorrected TOA reflectances (RC). In each case, a NN architecture consisting of3 layers of 15 neurons improved performances and data availability compared tocurrent state-of-the-art algorithms used in the region. The NN operating on TOAreflectance outperformed BOA and RC versions. By operating on TOA reflectancedata, the NN approach overcomes the common but difficult problem of atmosphericcorrection in coastal waters. Moreover, the NN provides data for regions whichother algorithms often mask out for turbid water or low zenith angle flags. Adistinguishing feature of the NN approach is generation of associated productuncertainties based on multiple resampling of the training data set to producea distribution of values for each pixel, and an example is shown for a coastaltime series in the North Sea. The final output of the NN approach consists of abest-estimate image based on medians for each pixel and a second imagerepresenting uncertainty based on standard deviation for each pixel, providingpixel-specific estimates of uncertainty in the final product.

An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance