Abstract
In remote sensing sea surface temperature (SST), the traditional fusion method is used to compute the dot product of a subjective weight vector with a satellite measurement vector, while the result requires validation by field measurement. However, field measurement that relative to the satellite measurement is very sparse, many information may not be verified. A relative objective weight vector is constructed by using the limited field measurement, which is based on coefficient of variation method. And then it make an application of the data fusion by the weighted average method in the SST data. fuse SST data with the weighted average method. In this way, some posteriori information can be added to the fusion process. The model reduces the dependence on verification, and some of the satellite measurement can be handled without corresponding to the field measurement, and the fusion result matches transfer errors theory.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Hosoda H, Murakami H, Shibata A, Sakaida F, Kawamura H. Difference characteristics of sea surface temperature observed by GLI and AMSR aboard ADEOS-II [J]. Journal of Oceanography, 2006, 62(5): 339–350.
Stammer D, Wentz F, Gentemann C. Validation of microwave sea surface temperature measurements for climate purposes [J]. Journal of Climate, 2003, 16(1): 73–87.
Iwasaki S, Kubota M, Tomita H. Inter-comparison and evaluation of global sea surface temperature products [J]. International Journal of Remote Sensing, 2008, 29(21): 500–512.
Barton I, Pearce A. Validation of GLI and other satellite-derived sea surface temperatures using data from the Rottnest Island Ferry, Western Australia [J]. Journal of Oceanography, 2006, 62(3): 303–310.
Rawat S, Linas J, Bowman C. Design of a performance evaluation methodology for data fusion-based multiple target tracking systems [C]// Proceedings of SPIE, Bellingham WA. 2003, DOI:10.1117/12.497973.
Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases [C]// Proceedings of the International Conference on Management of Data, Washington, USA. 1993: 207–216.
Zhu H L, Xu Z G. An effective algorithm for mining positive and negative association rules [C]// Proceedings of the International Conference on Computer Science and Software Engineering, Wuhan, China. 2008:455–458.
Fei Ye-tai. Error theory and data processing [M]. Hefei: China Machine Press, 2005 (in Chinese).
Liu Na. The research on the method of extension correct point for multi-satellite SST [D]. Doctoral dissertation, Shanghai: Shanghai University, 2011 (in Chinese).
Xie Y N, Zhou X L, Yang Z D. Error analysis of non-spherical raindrops on precipitation measurement [J]. Journal of Shanghai University (English Edition), 2011, 15(2): 92–95.
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the National Natural Science Foundation of China (Grant No.40976108), and the Shanghai Leading Academic Discipline Project (Grant No.J50103)
About this article
Cite this article
Zhong, F., Liu, N., Liu, Y. et al. New SST correction method from multi-satellite based on the coefficient of variation. J. Shanghai Univ.(Engl. Ed.) 15, 463–466 (2011). https://doi.org/10.1007/s11741-011-0769-3
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11741-011-0769-3