Satellite-Based Estimation of Column-Integrated Algal Biomass in Nonalgae Bloom Conditions: A Case Study of Lake Chaohu, China

Abstract

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

A Remote Sensing Algorithm of Column-Integrated Algal Biomass Covering Algal Bloom Conditions in a Shallow Eutrophic Lake

“…Thus, the algorithm was less sensitive to depth than algal biomass integrated from the surface to 40 cm. This revealed different results under non-algal bloom conditions, where surface investigation was less sensitive to water depth [54].…”

“…For each survey, field measurements focused on the vertical distribution of Chl-a concentration and remote sensing reflectance. Water samples to retrieve Chl-a concentrations were collected at nine different depths (surface, 0.1, 0.2, 0.4, 0.7, 1.0, 1.5, 2 and 3 m) using a customized profiling instrument [54]. The instrument consists of four parts: a graduated profiling sampling wire, a small vacuum pump, a connective tube and a portable battery.…”

“…As the water depth of Lake Chaohu is less than 6 m, the algorithm was built to include algal biomass up 6 m [54]. A good linear relationship was observed between Bio(40) and column integrated algal biomass at different depth layers, with R 2 = 0.81, NRMSD = 0.43.…”

“…It was calculated based on hydrological data and DEM data of Lake Chaohu. More details on water depth calculation were described in the previous paper [54].…”

“…In eutrophicated Case II waters, Chl-a concentration can be extremely high, further complicating the vertical distribution. Past studies have shown that surface information and local physical/hydrologic conditions, such as water depth, influences the vertical profile for Case II waters [53,54]. However, this relationship has been retrieved limited to the non-bloom condition.…”