Abstract
Abstract An ocean-related geophysical parameter estimation method has been proposed. The method uses a geographic information system (GIS) as a neural network (NN). It was found that the proposed GIS-NN allows geophysical parameter estimations with the most appropriate coefficients for the specified areas and seasons. In comparison to the widely used sea surface temperature (SST) estimation method of multichannel SST (MCSST) with general use of the coefficients for the regression equation using Advanced Very High Resolution Radiometer (AVHRR) bands 4 and 5 from the National Oceanic and Atmospheric Administration (NOAA) satellite, it was found that the proposed method achieves a significant reduction in root mean square error (from 0.315 to 0.245 K). It was also found that the proposed method requires 2.63 times of computation power in comparison with the MCSST method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
