论文部分内容阅读
The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many fields including fisheries,the military,and industry.Satellite-derived SST images have been used widely for front detection,although these data are susceptible to influence by many objective factors such as clouds,which can cause missing data and a reduction in front detection accuracy.However,front detection in a single SST image cannot fully reflect its temporal variability and therefore,the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time.In this paper,an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more effectively.Through experiments based on MODIS Terra and Aqua data,we verified that fronts could be distinguished better by using the proposed algorithm.Additionally through its use,we analyzed the monthly variations of fronts in the Bohai,Yellow,and East China Seas,based on Terra data from 2000 to 2013.
The oceanic front is a narrow zone in which water properties change abruptly within a short distance. The sea surface temperature (SST) front is an important type of oceanic front, which plays a significant role in many fields including fisheries, the military, and industry .Satellite-derived SST images have been used widely for front detection, although these data are susceptible to influence by many objective factors such as clouds, which can cause missing data and a reduction in front detection accuracy. Before, front detection in a single SST image can not fully reflect its temporal variability and therefore, the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time.In this paper, an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more effectively. Based on MODIS Terra and Aqua data, we verified that fronts could be distinguished better by using t he proposed algorithm .Additionally through its use, we analyzed the monthly variations of fronts in the Bohai, Yellow, and East China Seas, based on Terra data from 2000 to 2013.