Application of Remote Sensing to Identify and Monitor Seasonal and Interannual Changes of Water Turbidity in Yellow River Estuary, China

Abstract

AbstractWater turbidity is an important indicator for water security and environmental security in the Yellow River estuary. However, due to the complex terrain and harsh climatic environment, it is difficult to monitor the water turbidity over the complex surface of the estuary. In this study we applied a self‐organizing map clustering method, an artificial neural network clustering method, to extract turbidity patterns from the long‐term remote sensing data sets in the Yellow River estuary. Based on the Moderate Resolution Imaging Spectroradiometer data from 2000 to 2015, six turbidity patterns were identified by using the self‐organizing map clustering method: high turbidity pattern, moderate turbidity pattern, low turbidity pattern, very low turbidity pattern, extreme high turbidity pattern and sea ice pattern, and the first four patterns appear every year. All patterns have significant seasonal characteristics, and monthly turbidity is dominated by one of these turbidity patterns. The water turbidity in the estuary has decreased in the past 16 years, and the interannual variation of the turbidity pattern is the result of the combination of the sediment transported into the sea by the Yellow River and the wind and waves on the sea surface.