Satellite remote sensing can be an effective alternative for mapping cyanobacterial scums and aquatic macrophyte distribution over large areas compared with traditional ship's site-specific samplings. However, similar optical spectra characteristics between aquatic macrophytes and cyanobacterial scums in red and near infrared (NIR) wavebands create a barrier to their discrimination when they co-occur. We developed a new cyanobacteria and macrophytes index (CMI) based on a blue, a green, and a shortwave infrared band to separate waters with cyanobacterial scums from those dominated by aquatic macrophytes, and a turbid water index (TWI) to avoid interference from high turbid waters typical of shallow lakes. Combining CMI, TWI, and the floating algae index (FAI), we used a novel classification approach to discriminate lake water, cyanobacteria blooms, submerged macrophytes, and emergent/floating macrophytes using MODIS imagery in the large shallow and eutrophic Lake Taihu (China). Thresholds for CMI, TWI, and FAI were determined by statistical analysis for a 2010-2016 MODIS Aqua time series. We validated the accuracy of our approach by in situ reflectance spectra, field investigations and high spatial resolution HJ-CCD data. The overall classification accuracy was 86% in total, and the user's accuracy was 88%, 79%, 85%, and 93% for submerged macrophytes, emergent/floating macrophytes, cyanobacterial scums and lake water, respectively. The estimated aquatic macrophyte distributions gave consistent results with that based on HJ-CCD data. This new approach allows for the coincident determination of the distributions of cyanobacteria blooms and aquatic macrophytes in eutrophic shallow lakes. We also discuss the utility of the approach with respect to masking clouds, black waters, and atmospheric effects, and its mixed-pixel effects.

In shallow lakes, algal biomass is a fundamental indicator of eutrophication status. However, the vertical movement of phytoplankton within the water column can complicate the determination of total phytoplankton biomass using remotely sensed data of surface conditions. In this study, we develop, validate, and apply a new approach to use remotely sensed reflectance to estimate the variability of total algal biomass in shallow eutrophic lakes. Using the baseline normalized difference bloom index together with hydrological and bathymetric data, we determine the spatial and temporal dynamics of the total algal biomass in Lake Chaohu, a large shallow lake in eastern China under the nonalgae bloom conditions. Over an eleven-year period (2003-2013), the total phytoplankton biomass was highly variable, more than doubling between 2006 and 2007, from 19.95t to 39.50t. The seasonal decomposition of biomass dynamics indicated the highest biomass production occurred in June, while the lowest occurred in April. The estimates of total phytoplankton biomass were both consistent with in situ measurements and consistent for observations made on the same day and on consecutive days. The improved stability and reliability of total biomass estimations provided more complete information about lake conditions with respect to surface concentrations. This has implications for both management and modeling

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