Abstract
The extension and frequency of algal blooms in surface waters can be monitored using remote sensing techniques, yet knowledge of their vertical distribution is fundamental to determine total phytoplankton biomass and understanding temporal variability of surface conditions and the underwater light field. However, different vertical distribution classes of phytoplankton may occur in complex inland lakes. Identification of the vertical profile classes of phytoplankton becomes the key and first step to estimate its vertical profile. The vertical distribution profile of phytoplankton is based on a weighted integral of reflected light from all depths and is difficult to determine by reflectance data alone. In this study, four Chla vertical profile classes (vertically uniform, Gaussian, exponential and hyperbolic) were found to occur in three in situ vertical surveys (28 May, 19–24 July and 10–12 October) in a shallow eutrophic lake, Lake Chaohu. We developed and validated a classification and regression tree (CART) to determine vertical phytoplankton biomass profile classes. This was based on an algal bloom index (Normalized Difference algal Bloom Index, NDBI) applied to both in situ remote sensing reflectance (Rrs) and MODIS Rayleigh-corrected reflectance (Rrc) data in combination with data of local wind speed. The results show the potential of retrieving Chla vertical profiles information from integrated information sources following a decision tree approach.
Highlights
The eutrophication of coastal and inland waters is a major environmental and social-economic problem around the world
Four Chla vertical profile classes with different structure parameters were observed in Lake Chaohu, and the results indicated that the Chla vertical class may change in short times
The results suggested that Chla vertical distribution was spatially heterogeneous and helped to explain why the area and intensity of algal blooms changed over a short time
Summary
The eutrophication of coastal and inland waters is a major environmental and social-economic problem around the world. Remote sensing has been widely used to monitor the extent and frequency of algal blooms [6,7,8] as well as determine key optically active substances, such as chlorophyll-a concentrations (Chla) [9,10], and phycocyanin concentrations (PC) [10,11]. These estimates assume that the distribution of phytoplankton is vertically uniform or at least vertically consistent, leading to inaccurate estimates of total phytoplankton biomass across depth. Using the Geostationary Ocean Color Imager (GOCI), algal blooms in the East China Sea increased 100%
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