Estimation of chlorophyll-a concentration using an artificial neural network (ANN) - based algorithm with OCEANSAT-1 OCM data - Nagamani, P V; Chauhan, P; Dwivedi, R M (Photonirvachak (Dehra Dun), 2007)
An artificial neural network (ANN) based chlorophyll-a algorithm was developed to estimate chlorophyll-a concentration using OCEANSAT-1 Ocean Colour Monitor (OCM) satellite-data. A multi-layer perception (MLP) type neural network was trained using simulated reflectances ( similar to 60,000 spectra) with known chlorophyoll-a concentration, corresponding to the first five spectral bands of OCM. The correlation coefficient (r super(2)) and RMSE for the log transformed training data was found to be 0.99 and 0.07, respectively. The performance of the developed ANN-based algorithm was tested with the global SeaWiFS Bio-optical Algorithm Mini Workshop (SeaBAM) data ( similar to 919 spectra), 0.86 and 0.13 were observed as r super(2) and RMSE for the test data set. The algorithm was further validated with the in-situ bio-optical data collected in the northeastern Arabian Sea ( similar to 215 spectra), the r super(2) and RMSE were observed as 0.87 and 0.12 for this regional data set. Chlorophyll-a images were generated by applying the weight and bias matrices obtained during the training, on the normalized water leaving radiances (nL sub(w)) obtained from the OCM data after atmospheric correction. The chlorophyll-a image generated using ANN based algorithm and global Ocean Chlorophyll-4 (OC4) algorithm was compared. Chlorophyll-a estimated using both the algorithms showed a good correlation for the open ocean regions. However, in the coastal waters the ANN algorithm estimated relatively smaller concentrations, when compared to OC4 estimated chlorophyll-a.