Artificial neural networks (ANN) based algorithms for chlorophyll estimation in the Arabian Sea

Abstract

In-situ bio-optical measurements were collected during six ship campaigns in the north eastern Arabian Sea using SeaWiFS Multi-channel Profiling Radiometer (SPMR). An artificial neural network (ANN) based algorithms were constructed to estimate oceanic chlorophyll concentration using in-situ data. The different ANNs were obtained by systematic variations of architecture of input and hidden layer nodes for the Arabian Sea training data set. The performance of individual ANN-based pigment estimation algorithm was evaluated by applying it to the remote sensing reflectance data contained in validation data set. The performance of the most successful ANN was compared with commonly used empirical pigment algorithms. Compared to e.g. the SeaWiFS algorithms Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4), the square of the correlation coefficient r2 is increased from 0.69 for OC4, respectively 0.70 for OC2 to 0.96 for ANN algorithm. The RMS error of the estimated log-transformed pigment concentration dropped from 0.47 for OC2, respectively 0.41 for OC4 to 0.11 for ANN-based pigment algorithm.