A Combination of Spatial Domain Filters to Detect Surface Ocean Current from Multi-Sensor Remote Sensing Data

AS, Mohammed Abdul Athick; Lee, Shih‐Yu

doi:10.3390/rs14020332

Cited by 3 publications

(5 citation statements)

References 112 publications

(156 reference statements)

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“…Initially, we employed the methodology of using a combination of spatial domain filters [38] to delineate Kuroshio current from its neighboring water masses as the method recommended by Wang [95]. Liu and Hou [96] did not effectively unveil Kuroshio in both xand y-directions and as well as in summer due to uniform heating.…”

Section: Detection Of Kc Frontmentioning

confidence: 99%

“…The Kuroshio front was generally identified by researchers in the conventional rectangular or square domain. The two-dimensional histogram, in conjunction with the SST and chlorophyll-a ranges, was used by Takahashi and Kawamura to analyze the seasonal fluctuation of Kuroshio [37,38]. However, the objective technique [39] is capable of spatially detecting ocean fronts.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, extracting features by just specifying an array of values refrains from defining the precise border of Kuroshio [38]. Boundary identification, cluster shadow method, and entropic approach are the most efficient and functional techniques for separating a feature from its surroundings.…”

Section: Introductionmentioning

confidence: 99%

“…These image processing approaches to spatially identify a feature precisely in remote sensing satellite data are incomprehensible to recognize. In a similar vein, the histogram equalization and gradient detection filter are two extensively used techniques because of their robustness, sufficient global validation, simplicity, and ease of use [38].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2.Overall methodology, employed in this study to delineate KC followed by Time series decomposition and Trend analysis[38].…”

mentioning

confidence: 99%

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Comprehensive Analysis of Ocean Current and Sea Surface Temperature Trend under Global Warming Hiatus of Kuroshio Extent Delineated Using a Combination of Spatial Domain Filters

Lee

2022

Geomatics

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The effect of climate prevails on a diverse time scale from days to seasons and decades. Between 1993 and 2013, global warming appeared to have paused even though there was an increase in atmospheric greenhouse gases. The variations in oceanographic variables, like current speed and sea surface temperature (SST), under the influence of the global warming hiatus (1993–2013), have drawn the attention of the global research community. However, the magnitude of ocean current and SST characteristics oscillates and varies with their geographic locations. Consequently, investigating the spatio-temporal changing aspects of oceanographic parameters in the backdrop of climate change is essential, specifically in coastal regions along Kuroshio current (KC), where fisheries are predominant. This study analyzes the trend of ocean current and SST induced mainly during the global warming hiatus, before and till the recent time based on the daily ocean current data from 1993 to 2020 and SST between 1982 and 2020. The Kuroshio extent is delineated from its surrounding water masses using an aggregation of raster classification, stretching, equalization, and spatial filters such as edge detection, convolution, and Laplacian. Finally, on the extracted Kuroshio extent, analyses such as time series decomposition (additive) and statistical trend computation methods (Yue and Wang trend test and Theil–Sen’s slope estimator) were applied to dissect and investigate the situations. An interesting downward trend is observed in the KC between the East coast of Taiwan and Tokara Strait (Tau = −0.05, S = −2430, Sen’s slope = −5.19 × 10−5, and Z = −2.61), whereas an upward trend from Tokara Strait to Nagoya (Tau = 0.89, S = 4344, Sen’s slope = 8.4 × 10−5, and Z = 2.56). In contrast, a consistent increasing SST in trend is visualized in the southern and mid-KC sections but with varying magnitude.

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Section: Detection Of Kc Frontmentioning

confidence: 99%