This chapter developed a deep-learning (DL) model for floating U. prolifera detection in the Yellow Sea based on the U-Net framework with overfitting prevention. Based on the 1,055/4,071 pairs of labelled samples, the model reached an accuracy of 97.51 (99.83)% and an Intersection over Union (IoU) of 42.62 (88.09)% for the MODIS (SAR) images. We processed satellite images containing U. prolifera using the DL model and drew an exciting finding: since SAR (MODIS) detect the floating (and submerged) parts of U. prolifera respectively, we defined a floating and submerged ratio number (FS ratio) to be a good indicator for representing different life phases of U. prolifera algae.
Abstract Brown tides of pelagophyte Aureococcus anophagefferens have been recorded in the coastal waters of Qinhuangdao in the Bohai Sea (BS) since 2009. Current understandings on the dynamics and mechanisms of brown tides in the BS, however, are still quite limited due to the lack of accurate methods for detecting low‐abundance of A. anophagefferens cells. In this study, a Taqman qPCR method was developed, which targeted 28S rDNA D1‐D2 domain of A. anophagefferens . The specificity of the qPCR method was tested using 25 strains of microalgae commonly present in the coastal waters, and the targeted species A. anophagefferens was the only species that had reliable positive signal. The method was highly sensitive and reproducible, and the lower detection limit could reach 0.3 cells of A. anophagefferens . A calibration curve was established for A. anophagefferens using the DNA template extracted from different number of cells, and the lower and upper limits of detection for the qPCR assay is 0.3 cells and 1.2 × 10 5 cells, respectively. A high recovery rate ranged from 96.5% to 107% was achieved for A. anophagefferens at different cell densities. The qPCR assay has been successfully applied to the study on spatial and temporal variations of A. anophagefferens in the coastal waters of Qinhuangdao in 2014.
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