Identification and assessment of the drift velocity of green tides using the maximum cross-correlation method in the Yellow Sea

Menghao Ji; Chengyi Zhao; Xin Dou; Can Wang; Dian Zhou; Jianting Zhu

doi:10.1016/j.marpolbul.2023.115420

Identification and assessment of the drift velocity of green tides using the maximum cross-correlation method in the Yellow Sea

Mar Pollut Bull. 2023 Sep;194(Pt A):115420. doi: 10.1016/j.marpolbul.2023.115420. Epub 2023 Aug 24.

Authors

Menghao Ji¹, Chengyi Zhao², Xin Dou¹, Can Wang¹, Dian Zhou¹, Jianting Zhu³

Affiliations

¹ School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China.
² School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China. Electronic address: zhaocy@nuist.edu.cn.
³ Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA.

PMID: 37632984
DOI: 10.1016/j.marpolbul.2023.115420

Abstract

The green tides outbreak events seriously threaten the ecological balance of the coastal areas. Quickly and accurately obtaining the spatial distribution and drift state of green tides is key to early warning. Based on Landsat 8 (L8) and Sentinel-2 (S2) image pair, the green tides drift velocity was extracted using the maximum cross-correlation (MCC) method, and windage was calculated by combining ocean current and wind data. The results of the MCC method were validated. Ulva's drift in the Yellow Sea is shaped by both ocean currents and wind, closely aligning with the direction of the currents. Notably, the northward drift velocity of Ulva exhibits a clear boundary around 34°40'N. Windage shows similar characteristics with the Ulva drift velocity, as its values vary with time and space. This study will enhance our comprehension of the dynamic mechanism of green tides drift.

Keywords: Drift velocity; Green tides; Maximum cross-correlation method (MCC); Windage.

MeSH terms

Ulva*
Wind