Neural network-based estimation of chlorophyll-a concentration in coastal waters

Mohamad T. Musavi; Richard L. Miller; Habtom Ressom; Padma Natarajan

doi:10.1117/12.452814

14 January 2002 Neural network-based estimation of chlorophyll-a concentration in coastal waters

Mohamad T. Musavi, Richard L. Miller, Habtom Ressom, Padma Natarajan

Proceedings Volume 4488, Ocean Optics: Remote Sensing and Underwater Imaging; (2002) https://doi.org/10.1117/12.452814
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

The estimation of chlorophyll-a is one of the key indices of monitoring the phytoplankton populations. In this paper, an approach for estimating chlorophyll-a concentration using a neural network model is prose. A dat set assembled form various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop containing coincident in-situ chlorophyll and remote sensing reflectance measurements is used to evaluate the efficacy of the proposed neural network model. The data comprises of 919 stations and has chlorophyll-a concentrations ranging between 0.019 and 32.79 (mu) g/l. There are approximately 20 observations form more turbid coastal waters. A feed-forward neural network model with 10 noes in the hidden layer has been constructed to estimate chlorophyll-a concentration. The remote sensing reflectances form five SeaWiFS wavelengths are used as inputs to our model. The network is trained using the Levenberg-Marquardt algorithm. A neural network model can deal with non-linear relationships more accurately. Neural networks can effectively include variables that tend to co-vary non- linearly relationships more accurately. Neural networks can effectively include variables that tend to co-vary non- linearly with the output variable. They are flexible towards the choice of inputs and are tolerant to noise and require no a priori knowledge about the effect of these parameters. This makes them an ideal candidate for estimating chlorophyll-a concentration in coastal waters, where the presence of suspended sediments, detritus, and dissolved organic matter creates an optically complex situation. By allowing the neural network model to include several optical parameters as additional inputs to account for the scattering and absorption phenomena the model can be extended to estimate chlorophyll-a concentration turbid coastal waters.

Citation Download Citation

Mohamad T. Musavi, Richard L. Miller, Habtom Ressom, and Padma Natarajan "Neural network-based estimation of chlorophyll-a concentration in coastal waters", Proc. SPIE 4488, Ocean Optics: Remote Sensing and Underwater Imaging, (14 January 2002); https://doi.org/10.1117/12.452814

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