TY - JOUR
T1 - A comparison of satellite-derived sea surface temperature fronts using two edge detection algorithms
AU - Chang, Yi
AU - Cornillon, Peter
N1 - Funding Information:
This study was supported by a research Grant ( NSC96-2917-I-019-102 ) from the National Science Council, Taiwan . Salary support for P. Cornillon was provided by the State of Rhode Island and Providence Plantations . The authors wish to thank Prof. Hiroshi Kawamura, Dr. Futoki Sakaida, and Dr. Teruhisa Shimada of Tohoku University, Japan, for their technical support and advice and Dr. Igor Belkin of the University of Rhode Island, USA, for helpful advice on this manuscript.
Publisher Copyright:
© 2013 Elsevier Ltd.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - Satellite-derived sea surface temperature (SST) fronts provide a valuable resource for the study of oceanic fronts. Two edge detection algorithms designed specifically to detect fronts in satellite-derived SST fields are compared: the histogram-based algorithm of Cayula and Cornillon (1992, 1995) and the entropy-based algorithm of Shimada et al. (2005). The algorithms were applied to 4 months (July and August for both 1995 and 1996) of SST fields and the results are compared with SST data taken by the M.V. Oleander, a container ship that makes weekly transits between New York and Bermuda. There is no significant difference in front pixels found with the Cayula-Cornillon algorithm and those found in the in situ (Oleander) data. Furthermore, for strong fronts, with gradients greater than 0.2. K/km, the distribution of fronts found with the Shimada et al. algorithm is quite similar to that of fronts found with the Cayula-Cornillon algorithm. However, there are significant differences in the number of weak fronts found. This is seen clearly in waters south of the Gulf Stream where the gradient magnitude of fronts found is less than 0.1. K/km. In this region, the probability that the Shimada et al. algorithm detects a front rarely falls below 4% while neither the Cayula-Cornillon algorithm applied to the satellite-derived SST fields nor the gradient-based algorithm applied to the Oleander temperature time series find fronts more than 1% of the time. These results raise the question of exactly what qualifies as an SST front, a classic problem in edge detection.
AB - Satellite-derived sea surface temperature (SST) fronts provide a valuable resource for the study of oceanic fronts. Two edge detection algorithms designed specifically to detect fronts in satellite-derived SST fields are compared: the histogram-based algorithm of Cayula and Cornillon (1992, 1995) and the entropy-based algorithm of Shimada et al. (2005). The algorithms were applied to 4 months (July and August for both 1995 and 1996) of SST fields and the results are compared with SST data taken by the M.V. Oleander, a container ship that makes weekly transits between New York and Bermuda. There is no significant difference in front pixels found with the Cayula-Cornillon algorithm and those found in the in situ (Oleander) data. Furthermore, for strong fronts, with gradients greater than 0.2. K/km, the distribution of fronts found with the Shimada et al. algorithm is quite similar to that of fronts found with the Cayula-Cornillon algorithm. However, there are significant differences in the number of weak fronts found. This is seen clearly in waters south of the Gulf Stream where the gradient magnitude of fronts found is less than 0.1. K/km. In this region, the probability that the Shimada et al. algorithm detects a front rarely falls below 4% while neither the Cayula-Cornillon algorithm applied to the satellite-derived SST fields nor the gradient-based algorithm applied to the Oleander temperature time series find fronts more than 1% of the time. These results raise the question of exactly what qualifies as an SST front, a classic problem in edge detection.
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U2 - 10.1016/j.dsr2.2013.12.001
DO - 10.1016/j.dsr2.2013.12.001
M3 - Article
AN - SCOPUS:84941261650
VL - 119
SP - 40
EP - 47
JO - Deep-Sea Research Part II: Topical Studies in Oceanography
JF - Deep-Sea Research Part II: Topical Studies in Oceanography
SN - 0967-0645
ER -