In recent years, with increasing human activities, water eutrophication has seriously threatened the ecological security of lakes. In order to obtain the range of algal bloom quickly and accurately, FAI linear fitting model of planktonic algae index (FAI-L) is proposed in this paper. In previous studies, NDVI (normalized difference vegetation index) has been widely used in algal bloom identification, and the method of obtaining NDVI threshold by slope calculation has also been verified. Compared with NDVI, FAI is less sensitive to changes in environmental conditions and has higher accuracy in the identification of algal blooms due to the addition of short infrared bands to effectively reduce the impacts of atmosphere and thin clouds. However, there is no effective solution to the problem of how to determine the threshold for identifying algal blooms by FAI. This paper established the linear fitting equation between NDVI and FAI and used NDVI threshold to determine FAI threshold, which can effectively solve the problem of FAI threshold determination. The extraction results of Landsat8 and Sentinel-2 showed that: (1) Compared with NDVI, the accuracy of FAI-L was greatly improved. The extraction accuracy of algal blooms from Landsat8 images was 97.16%, which was 5.44% higher than that of NDVI (91.72%). (2) Based on Sentinel-2 data, the applicability of FAI-L was explored. The results showed that for high-resolution remote sensing images, the extraction accuracy of FAI-L was 97.10%, and had good applicability. (3) FAI-L could eliminate the interference of clouds on the extraction of algal blooms and had a better extraction effect on the edge areas and small sporadic areas of algal blooms. FAI-L can better obtain the spatial distribution of algal blooms in Lake Chaohu waters, effectively reduce the misjudgment caused by clouds, and has high accuracy and applicability. It can provide a new method for determining the threshold of the spectral sensitivity index of algal bloom extraction.