Annual dynamic remote sensing monitoring of phycocyanin concentration in Lake Chaohu based on Sentinel-3 OLCI images

Abstract

Phycocyanin (PC) is an indicator pigment of cyanobacteria in water, and its concentration reflects the biomass of cyanobacteria. Monitoring the annual dynamics of PC concentration by satellite remote sensing is of great significance for the effective prevention and control of cyanobacteria bloom. Based on the measured data of phycocyanin concentration in Lake Chaohu in different seasons and the Sentinel-3 OLCI images of the same period, a machine learning regression retrieval model was constructed and applied to the 2019 OLCI image set of Lake Chaohu to monitor the spatial distribution and annual variation of PC concentration. The results show that among MUMM and C2RCC atmospheric correction methods, the atmospheric correction result of C2RCC is closer to the measured spectral reflectance. Among the machine learning regression algorithms, the retrieval model of PC concentration based on gradient boosting regression has the highest accuracy, its R2, RMSE and rRMSE reach 0.84, 49.76 μg/L and 34.1%, respectively. The concentration of PC was low from January to April and December, but high from May to November and fluctuated frequently. The average daily temperature was the main reason for the annual variation of PC concentration, and the short-term fluctuation of PC was mainly affected by the daily precipitation and sunshine duration. In summer and autumn, the PC concentration in the West Lake area was significantly higher than that in the middle and east lake area, which was mainly related to the high input of nitrogen and phosphorus into the lake. Sentinel-3 OLCI images provide important data sources for the dynamic monitoring of PC concentration in lake and reservoir waters, and the gradient boosting regression algorithm has great application potential in the retrieval of PC concentration in eutrophication waters.