Development and application of a neural network based ocean colour algorithm in coastal waters
Abstract:An algorithm for determining chlorophyll-a concentrations in shallow, case II waters has been developed and applied to nearly six years of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data in order to observe the general chlorophyll-a patterns in a coastal estuarine environment. Due to the fact that the current empirical chlorophyll-a algorithm (OC4) used to process SeaWiFS data breaks down in coastal waters, a neural network based algorithm was developed. The neural network in the study uses SeaWiFS remote sensing reflectance data paired with in situ chlorophyll-a data in the Delaware Bay and its adjacent coastal zone (DBAC) from a number of different days and seasons in an effort to overcome the limitations of single day algorithms and simulated dataset algorithms. Although the neural network model (NN) in this study displayed some difficulty representing high chlorophyll-a values, it showed significant improvement over the OC4 algorithm. The performance parameters of the NN were an r 2 of 0.79, a root mean square (RMS) error of 3.69?mg m -3 and a relative RMS error of 0.77. The NN was used to reprocess approximately six years of cloud free imagery of the DBAC from which the spatial and temporal variability of the chlorophyll-a distributions in the DBAC were analysed. Time series of absolute chlorophyll-a values for five stations along the central axis of the Delaware Bay were analysed using Fourier analysis techniques, from which chlorophyll-a patterns were found to have a quasi-annual period. Furthermore, the spatial distributions of the chlorophyll-a patterns were analysed using a general climatology and monthly climatologies of normalized chlorophyll-a values. The climatologies generally agreed with spatial distributions determined from historic ship-based data. The study found that summer blooms in the mid-estuary of the Delaware Bay may be more important than previously observed. This suggests that more frequent and synoptic measurements via satellite can reveal important new information about even well studied regions.
Document Type: Research Article
Affiliations: Ocean Remote Sensing Institute, Ocean University of China, Qingdao, China
Publication date: 2005-03-01