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

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 proposed. A data set assembled from various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) 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 mug/l. There are approximately 20 observations from more turbid coastal waters. A feed-forward neural network model with 10 nodes in the hidden layer has been constructed to estimate chlorophyll-a concentration. The remote sensing reflectances from five SeaWiFS wavelengths are used as inputs to our model. The network is trained using the Levenberg-Marquardt algorithm. The results of the proposed model show a significant improvement over linear and other popular empirical models. 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 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.