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

Abstract

This research investigates the applicability of combining spatial filter’s algorithm to extract surface ocean current. Accordingly, the raster filters were tested on 80–13,505 daily images to detect Kuroshio Current (KC) on weekly, seasonal, and climatological scales. The selected raster filters are convolution, Laplacian, north gradient, sharpening, min/max, histogram equalization, standard deviation, and natural break. In addition, conventional data set of sea surface currents, sea surface temperature (SST), sea surface height (SSH), and non-conventional data such as total heat flux, surface density (SSD), and salinity (SSS) were employed. Moreover, controversial data on ocean color are included because very few studies revealed that chlorophyll-α is a proxy to SST in the summer to extract KC. Interestingly, the performance of filters is uniform and thriving for seasonal and on a climatological scale only by combining the algorithms. In contrast, the typical scenario of identifying Kuroshio signatures using an individual filter and by designating a value spectrum is inapplicable for specific seasons and data set. Furthermore, the KC’s centerlines computed from SST, SSH, total heat flux, SSS, SSD, and chlorophyll-α correlate with sea surface currents. Deviations are observed in the various segments of Kuroshio’s centerline extracted from heat flux, chlorophyll-α, and SSS flowing across Tokara Strait from northeast Taiwan to the south of Japan.

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

“…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.…”

“…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.…”

“…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.…”

“…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].…”

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