Study on the temporal and spatial distribution of chlorophyll a in Erhai Lake based on multispectral data from environmental satellites

Abstract Satellite remote sensing technology presents advantages of macroscopicity, timeliness and cost effectiveness and has been increasingly used in lake water quality monitoring. In this paper, an empirical model for the remote sensing inversion of the chlorophyll a (Chl-a) concentration in Erhai Lake was established using ground monitoring Chl-a concentration data and multispectral remote sensing data from environmental satellites from 2010 to 2017. The average absolute error and relative error were 1.92 mg/m3 and 22%, respectively. A 10-year remote sensing inversion of Erhai blooms identified the temporal and spatial distribution characteristics of blooms and showed that the occurrence frequency of blooms was 37%, and they were mainly in the form of light algal blooms at a local scale. Moderate and severe blooms occurred at a frequency of 42%, mainly from Oct. to Jan. Moderate algal blooms were distributed along the southern and northern coasts and in coastal areas, while severe algal blooms were distributed in the northern section and across the entire lake. During the bloom period, the growth rate of the bloom area reached 102 km2/d, which was faster than the reduction rate (90 km2/d). Bloom events generally lasted for 6–37 days. The inflow of pollution sources led to a higher frequency of blooms in the coastal zone than in the lake center, and the frequency in the northern section was nearly twice as high as that in the southern section. Most blooms in Erhai Lake occurred from late summer to winter (i.e., Jul. to Jan. of the following year) because of the higher average air temperature (AT) and lower wind speed (WS) in winter and spring and the amount of precipitation in summer and autumn. The remote sensing method captured the high-risk areas and the spatial-temporal evolution trend of algal blooms, and the model provided support for the prevention and control of lake algal blooms; however, this work should be complemented by ground monitoring data for cloudy days.

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