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Sensors 2011, 11(8), 7530-7544; doi:10.3390/s110807530

The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates

1
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 20224, Taiwan
2
Department of Marine Biotechnology and Resources, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
*
Author to whom correspondence should be addressed.
Received: 17 June 2011 / Revised: 20 July 2011 / Accepted: 20 July 2011 / Published: 29 July 2011
(This article belongs to the Section Remote Sensors)
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Abstract

An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%. View Full-Text
Keywords: infrared sensor; data mining; neural network; sea surface temperature; tropical pacific infrared sensor; data mining; neural network; sea surface temperature; tropical pacific
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Lee, Y.-H.; Ho, C.-R.; Su, F.-C.; Kuo, N.-J.; Cheng, Y.-H. The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates. Sensors 2011, 11, 7530-7544.

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