  Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic
for relatively turbid coastal waters due to the impact of non-algal materials
on atmospheric correction and standard Chl algorithm performance. Artificial
neural networks (NNs) provide an alternative approach for retrieval of Chl from
space and results in northwest European shelf seas over the 2002-2020 period
are shown. The NNs operate on 15 MODIS-Aqua visible and infrared bands and are
tested using bottom of atmosphere (BOA), top of atmosphere (TOA) and Rayleigh
corrected TOA reflectances (RC). In each case, a NN architecture consisting of
3 layers of 15 neurons improved performances and data availability compared to
current state-of-the-art algorithms used in the region. The NN operating on TOA
reflectance outperformed BOA and RC versions. By operating on TOA reflectance
data, the NN approach overcomes the common but difficult problem of atmospheric
correction in coastal waters. Moreover, the NN provides data for regions which
other algorithms often mask out for turbid water or low zenith angle flags. A
distinguishing feature of the NN approach is generation of associated product
uncertainties based on multiple resampling of the training data set to produce
a distribution of values for each pixel, and an example is shown for a coastal
time series in the North Sea. The final output of the NN approach consists of a
best-estimate image based on medians for each pixel and a second image
representing uncertainty based on standard deviation for each pixel, providing
pixel-specific estimates of uncertainty in the final product.
