Affiliation:
1. Science Systems and Applications, Inc.
2. NASA Goddard Space Flight Center
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.
Funder
National Aeronautics and Space
Administration
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献