Algal blooms happen widely in eutrophic lakes. The satellite-derived surface algal bloom area and chlorophyll-a (Chl-a) concentration are commonly used as indicators to judge the water quality. However, vertical migration of phytoplankton may seriously affect the assessment accuracy of water quality with the surface status as an indicator. In fact, the water column-integrated algal biomass is better for describing the water quality. To remotely estimate the algal biomass considering the vertical Chl-a profile (uniform or otherwise), this paper proposed a novel process-oriented algorithm applied successfully in Lake Chaohu, China. First, we built a decision tree to identify the pixel-based Chl-a profile types using the floating algae index, surface Chl-a concentration, and wind speed. Then, the nonuniform profile was expressed as a power decay function ($\mathit{Chl}-a(z)=n_1\times z^{n_2}$) and parameterized using the surface Chl-a concentration. Finally, the algal biomass was derived from MODIS/Aqua satellite data. The estimation results were acceptable and the bias was −19.95%. The monthly mean algal biomass varied significantly and exhibited two peaks in spring and summer. The annual mean algal biomass increased from 2003 to 2010 (Pearson r = 0.82; p < 0.05) and then decreased from 2012 to 2018 (r = −0.71; p = 0.07). Monthly variations were determined by the air temperature (77.09%), but annual variations were primarily influenced by ammonia nitrogen level (44.61%). With known algal biomass and wind speed, the pixel-based algal bloom probability could be forecasted. The developed process-oriented algorithm was also applicable to eutrophic Lake Taihu, China. This study is of great significance for scientifically assessing water quality and effectively managing aquatic environmental disasters.