An artificial neural network algorithm to retrieve chlorophyll a for Northwest European shelf seas from top of atmosphere ocean colour reflectance.
Hadjal, Madjid; Medina-Lopez, Encarni; Ren, Jinchang; Gallego, Alejandro; McKee, David
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 for 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 performance 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.
HADJAL, M., MEDINA-LOPEZ, E., REN, J., GALLEGO, A. and MCKEE, D. 2022. An artificial neural network algorithm to retrieve chlorophyll a for Northwest European shelf seas from top of atmosphere ocean colour reflectance. Remote sensing [online], 14(14), article 3353. Available from: https://doi.org/10.3390/rs14143353
|Journal Article Type||Article|
|Acceptance Date||Jul 1, 2022|
|Online Publication Date||Jul 12, 2022|
|Publication Date||Jul 31, 2022|
|Deposit Date||Jul 18, 2022|
|Publicly Available Date||Jul 18, 2022|
|Peer Reviewed||Peer Reviewed|
|Keywords||Artificial neural network; Ocean colour remote sensing; MODIS Aqua; Chlorophyll a; Top-of-atmosphere; North Sea; Coastal waters|
|Additional Information||A dataset for this output is hosted by Pureportal at University of Strathclyde and also contains details of projects connected with this output. The dataset is available from: https://doi.org/10.15129/32604fa3-ed09-4b5a-b735-9321ddaa8ef9|
HADIJAL 2022 An artificial neural network
Publisher Licence URL
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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