LAUSR

Coarse-resolution sensors have been used operationally to monitor floating algal blooms with a near daily revisit in coastal and inland waters. Most of the current methods in estimating fractional floating algae cover (FAC) were based on the linear pixel un-mixing assumption. In this study, a new FAC model following a logistic curve was developed and applied to multisensor satellite data in two large shallow eutrophic lakes, Lake Taihu and Lake Chaohu, in China. The results indicated that after resampling to 250 m, match-up pairs of Moderate Resolution Imaging Spectroradiometer (MODIS), The Visible and Infrared Imager/Radiometer Suite (VIIRS), Geostationary Ocean Color Imager (GOCI), and Ocean and Land Color Instrument (OLCI) possessed consistent Rayleigh corrected reflectance ( $R_{\mathrm {rc}}$ ) and floating algae index (FAI) or AFAI (alternative FAI). The FAC model was developed based on the simulated AFAI data of GOCI using point spread function (PSF) and bloom percent derived from Operational Land Imager (OLI) and then was applied to MODIS, VIIRS, and OLCI. Compared with the linear pixel un-mixing method, the FAC model reflects the asymptotic reflectance saturation in the near-infrared (NIR) band with the accumulation of blooms. Besides, the equivalent bloom area (EBA) of GOCI was validated using OLI-matched pairs with UPD 37.6% ( $N=39$ , $R^{2}=0.96$ ). The spatial-temporal dataset of FAC (2002–2020) shows that Lake Taihu and Lake Chaohu experienced severe algal blooms after 2010, partly resulting from the higher frequency of multisensor data. This study provides a method for building a lasting and comparable FAC dataset using multisensors.