Lacking of proper data source is becoming a problem for inland lake water environment remote sensing monitoring. To solve this problem, a bio- optical fusion (BOF) algorithm to merge multiband image and hyperspectral image of complex inland case 2 water is developed. The performance of the BOF algorithm is verified based on the bio-optical model of water body and traditional image fusion algorithm, by a Hyperion simulated dataset and compared with that of wavelet transform algorithm, Gram-Schmidt transforms algorithm, and color normalized algorithm. The results show that from the visual effect, the BOF algorithm has a better fusion of the color information of hyperspectral image and the detailed spatial information of the multispectral image. From the image evaluation indexes, the BOF algorithm gets the best performance under various resolution differences, and is not sensitive to the resolution difference; in the estimation experiment of chlorophyll- a concentration, the BOF algorithm also gets the best result, the root mean square deviation (RMSE) is 9.817, meanwhile, RMSE of the other three algorithms are 18.841,15.913 and 15.655. The proposed algorithm has strong potential applications, and expected to provide better data source for inland case 2 water remote sensing monitoring.