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Remote Sens. 2017, 9(11), 1204;

Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
Author to whom correspondence should be addressed.
Received: 6 November 2017 / Revised: 17 November 2017 / Accepted: 18 November 2017 / Published: 22 November 2017
(This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing)
PDF [12792 KB, uploaded 22 November 2017]


A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI) v2 daily 0.25° SST (OISST) products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed. View Full-Text
Keywords: sea surface temperature (SST); radial basis function network (RBFN); improved nearest neighbor cluster (INNC) algorithm sea surface temperature (SST); radial basis function network (RBFN); improved nearest neighbor cluster (INNC) algorithm

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Liao, Z.; Dong, Q.; Xue, C.; Bi, J.; Wan, G. Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks. Remote Sens. 2017, 9, 1204.

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