Quantifying Spatiotemporal Dynamics of the Column-Integrated Algal Biomass in Nonbloom Conditions Based on OLCI Data: A Case Study of Lake Dianchi, China

Traditional remote sensing observation can only derive chlorophyll-a concentration in surface water (Chla(surf)), which is insufficient for water quality monitoring or biogeochemical applications. In this paper, a column-integrated biomass (CIB) estimation algorithm was established in Lake Dianchi, China, and the CIB was estimated by Ocean and Land Color Instrument (OLCI) data from 2016 to 2018. First, the results show that the method is applicable to CIB estimation in nonbloom conditions [mean absolute percentage error (MAPE) = 10.55%, mean ratio (MR) = 0.996]. On the one hand, Chla(surf) could be estimated using a universal model of Chla (UMOC) estimation for case-II waters; on the other hand, CIB could be obtained from Chla(surf) according to the linear relationship between the Chla(surf) and CIB. Second, both the spatial and temporal variabilities of Chla(surf) are larger than those of CIB, which indicates that the biomass of the whole lake is relatively stable from day to day. During algal bloom periods, the CIB at nonbloom areas tends to decrease, suggesting that algae from nonbloom regions have migrated to bloom regions. Third, wind speed (WS) is the most critical meteorological factor affecting the water surface biomass, compared with other parameters. The average percentage of surface-to-total biomass in nonbloom conditions is about 7% when the WS is less than 2 m/s.