Instantaneous photosynthetically available radiation models for ocean waters using neural networks
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Kamal Aryal, Peng-Wang Zhai, Meng Gao, and Bryan A. Franz, "Instantaneous photosynthetically available radiation models for ocean waters using neural networks," Appl. Opt. 61, 9985-9995 (2022). https://doi.org/10.1364/AO.474914
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the
surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work,
we report two IPAR prediction models based on the neural network (NN) approach, one for open ocean and the
other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets
for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict
IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute the subsurface
IPAR profile very accurately up to the euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011 m−1
and 0.036 m−1
for open ocean and coastal waters,
respectively. The performance of the NN models is better than presently available semi-analytical models, with
significant superiority in coastal waters.