A Novel Algorithm to Estimate Algal Bloom Coverage to Subpixel Resolution in Lake Taihu

Remote sensing has often been used to monitor the distribution and frequency of floating algae in inland aquatic environments. However, due to the spatial resolution of the most common satellite sensors, accurate determination of algae coverage remains a major technical challenge. Here, a novel algorithm to estimate floating algae area to subpixel scales, denominated the algae pixel-growing algorithm (APA), is developed and evaluated for a series of image data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm utilizes the Rayleigh-corrected reflectance (Rrc) and a floating algae index (FAI) derived from Rrc in three spectral bands. Comparison with concurrent Landsat TM/ETM+ data indicate that the APA provides more accurate estimates of both algal bloom area and algal bloom intensity (i.e., floating algae coverage) (RSE = 15.2 km(2) and RE = 9.9%), compared to other commonly used methods such as the linear unmixing algorithm (LA). Furthermore, this study confirms that FAI is a better index with respect to normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) for the estimation of algae area coverage, especially when combined with the APA. Finally, the study provides a theoretical basis for the objective assessment of bloom severity in complex inland waterbodies.