As China’s first operational ocean color sensor, the coastal zone imager (CZI) carried by Haiyang-1C (HY-1C) satellite is playing an increasingly significant role in offshore ocean environmental monitoring. After the launch of HY-1D satellite, the combination of CZI sensors can provide two observations in three days for coastal zone through dual-satellite system. CZI sensors have demonstrated prominent application advantages for monitoring marine floating algae, oil spill and so on. Since the high spatial resolution optical data contains abundant information about the marine environment, it also brings some distraction to the identification and extraction of specific ocean targets. In this work, a novel CZI algorithm was developed based on cooperation of scaled algae index (SAI) and virtual baseline floating macroalgae height (VB-FAH) to extract floating green tide information in the Yellow Sea from HY-1C satellite CZI measurements. VB-FAH method can be used to enhance the difference between floating algae and sea water, especially for satellite’s sensors with no short-wave infrared bands. After that, the algorithm efficiently rejects the complex interference information in the ocean high spatial resolution optical data by SAI sliding window. The algorithm has high accuracy and time efficiency in the extraction of floating green tide from CZI measurements. Moreover, the study carried out an uncertainty analysis for the area of algae-containing pixels between HY-1C satellite CZI data with 50 m spatial resolution and GF-1 satellite WFV1 data with 16 m spatial resolution. The result indicates that the uncertainty in the inversion results of CZI data mainly comes from the over estimation of small patches of floating algae. The study also pointed out that the uncertainty of optical data for floating algae monitoring is not only from the difference of spatial resolution between two sensors, but also related to the spatial variability of the morphological size of floating algae. Exploring the morphological spatial variability of floating algae will help improve the accuracy of optical data inversion results and clarify the uncertainty.