Abstract: With the aim at solving the problem that adaptive threshold of vegetation index is not universal in the extraction of Enteromorpha prolifera from moderate resolution imaging spectroradiometer (MODIS) images, this paper proposed a multi-index decision fusion (MIDF) algorithm for Enteromorpha prolifera extraction. Firstly, the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), difference vegetation index (DVI) and ratio vegetation index (RVI) of the image were calculated, and then the local threshold method was used to segment the four gray-scale images of vegetation index to obtain the four initial coverage areas of Enteromorpha prolifera. Finally, the "absolute majority voting method" was used to vote on the four initial coverage areas of Enteromorpha prolifera, and the final result of Enteromorpha extraction was obtained. The experimental results showed that MIDF can adapt to the coverage areas of Enteromorpha with different densities, and performaned better than traditional NDVI, EVI, DVI and RVI adaptive threshold methods. MIDF is an unsupervised algorithm with a high level of automation, and can be applied to remote sensing operational monitoring of Enteromorpha disasters.