Geomorphic Evolution of Radial Sand Ridges in the South Yellow Sea Observed from Satellites

The radial sand ridges consist of more than 70 sand ridges that are spread out radially on the continental shelf of the South Yellow Sea. As a unique geomorphological feature in the world, its evolution process and characteristics are crucial to marine resource management and ecological protection. Based on the multi-source remote sensing image data from 1979 to 2019, three types of geomorphic feature lines, artificial coastlines, waterlines, and sand ridge lines were extracted. Using the GIS sequence analysis method (Digital Shoreline Analysis System (DSAS), spatial overlay analysis, standard deviational ellipse method), the evolution characteristics of the shoreline, exposed tidal flats, and underwater sand ridges from land to sea were interpreted. The results demonstrate that: (1) The coastline has been advancing towards the sea with a maximum advance rate of 348.76 m/a from Wanggang estuary to Xiaoyangkou Port. (2) The exposed tidal flats have decreased by 1484 km2 including the reclaimed area of 1414 km2 and showed a trend of erosion in the north around Xiyang channel and deposition in the southeast around the Gaoni and Jiangjiasha areas. (3) The overall sand ridge lines showed a trend of gradually moving southeast (135°), and the moving distance is nearly 4 km in the past 40 years. In particular, the sand ridge of Tiaozini has moved 11 km southward, while distances of 8 km for Liangyuesha and 5 km for Lengjiasha were also observed. For the first time, this study quantified the overall migration trend of the RSRs. The imbalance of the regional tidal wave system may be one of the main factors leading to the overall southeastward shift of the radiation sandbanks.

Dissolved Nitrous Oxide and Hydroxylamine in the South Yellow Sea and the East China Sea During Early Spring: Distribution, Production, and Emissions

Coastal marine systems are active regions for the production and emission of nitrous oxide (N2O), a potent greenhouse gas. Due to the inherently high variability in different coastal biogeochemical cycles, the factors and mechanisms regulating coastal N2O cycling remain poorly understood. Hydroxylamine (NH2OH), a potential precursor of N2O, has received less attention than other compounds in the coastal areas. Here, we present the spatial distribution of N2O and the first reported NH2OH distribution in the South Yellow Sea (SYS) and the East China Sea (ECS) between March and April 2017. The surface N2O concentrations in the SYS and the ECS varied from 5.9 to 11.3 nmol L–1 (average of 8.4 ± 1.4 nmol L–1) and were characterized by offshore and north–south decreasing gradients. NH2OH showed patchy characteristics and was highly variable, fluctuating between undetectable to 16.4 nmol L–1. We found no apparent covariation between N2O and NH2OH, suggesting the NH2OH pathway, i.e., nitrification (ammonium oxidation), was not the only process affecting N2O production here. The high NH2OH values co-occurred with the greatest chlorophyll-a and oxygen levels in the nearshore region, along with the relationships between NO2–, NO3–, and NH2OH, indicating that a “fresh” nitrifying system, favoring the production and accumulation of NH2OH, was established during the phytoplankton bloom. The high N2O concentrations were not observed in the nearshore. Based on the correlations of the excess N2O (ΔN2O) and apparent oxygen utilization, as well as ΔN2O vs. NO3–, we concluded that the N2O on the continental shelf was mainly derived from nitrification and nitrifier denitrification. Sea-to-air fluxes of N2O varied from −12.4 to 6.6 μmol m–2 d–1 (−3.8 ± 3.7 μmol m–2 d–1) using the Nightingale et al. (2000) formula and −13.3 to 6.9 μmol m–2 d–1 (−3.9 ± 3.9 μmol m–2 d–1) using the Wanninkhof (2014) formula, which corresponds to 75–112% in saturation, suggesting that the SYS and the ECS acted overall as a sink of atmospheric N2O in early spring, with the strength weakening. Our results reveal the factors and potential mechanisms controlling the production and accumulation of NH2OH and N2O in the SYS and the ECS during early spring.

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.