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Abstract Whitecaps generated by wave breaking and air entrainment can be classified as active (stageA) or residual (stageB). Measurement of each stage individually is essential for accurate parameterization of air–sea interaction processes, but conventional methods used for separation in visible images are subjective. In this study, this problem is solved using a pipeline for active whitecap fraction measurement. In this pipeline, a new horizon detection method is developed to stabilize and rectify images, and a deep learning model based on U-Net is trained and validated to identify and extract active whitecaps. The model demonstrates robust prediction accuracy even when images are contaminated by sun glint. The model is applied to 48 h of video footage collected during a cruise in Gulf of Mexico. It is determined that, as a function of wind speed, the active whitecap fraction has significant variability and disparity compared to previous research. This finding indicates that secondary factors should be considered for accurate whitecap parameterization. This is explored using a random forest, which indicates that sea surface temperature, swell, and wave age are important to the active whitecap fraction. The precise impact of sea surface temperature is further explored using analyses of variance (ANOVA), which suggest it has a positive correlation with the active whitecap fraction.
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A Novel Method to Discriminate Active from Residual Whitecaps Using Particle Image Velocimetry
Whitecap foam generated by wind-driven wave breaking is distinguished as either active (stage A) or residual (stage B). Discrimination of whitecap stages is essential to quantify the influence of whitecaps on the physical and chemical processes at the marine boundary layer. This study provides a novel method to identify whitecap stages based on visible imagery using particle image velocimetry (PIV). Data used are from a Gulf of Mexico cruise where collocated infrared (IR) and visible cameras simultaneously recorded whitecaps. IR images were processed by an established thresholding method to determine stage A lifetime from brightness temperature. The visible images were also filtered using a thresholding method and then processed using PIV to estimate the average whitecap velocity. A linear relationship was established between the lifetime of stage A and the timescale of averaged velocity. This novel method allows stage A whitecap lifetime to be determined using whitecap velocity and provides an objective approach to separate whitecap stages. This method paves the way for future research to easily quantify whitecap stages using affordable off-the-shelf video cameras. Results, which include evidence that whitecaps stop advancing before stage A ends and may be an indication of bubble plume degassing, are discussed.
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- Award ID(s):
- 1829986
- PAR ID:
- 10387934
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 20
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 4051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/rs13204051
