We examine the selection criteria for satellite images and methods of processing them in the process of mapping underwater landscapes using remotely sensed data, discuss the interpretation principles and algorithms as well as some issues related to the support of observations with field material. It is shown that a detailed landscape mapping of maritime shallow waters by methods of visual and automated interpretation requires multispectral superhigh spatial resolution images. Results of investigations made on underwater profiles by using lightweight diving outfits were employed to describe seven types of underwater landscapes, and echo sounder measurements were used in constructing the digital elevation model for the bottom of Srednyaya Bay. It is established that the regions for which it was possible to carry out a reliable interpretation of data from the IKONOS-2 spacecraft are in the range of depths between 0 and 10 m and make up about three-fourths of the area of the bay bottom. Ten facies were identified and put on the map, for each of which we determined the area, the range of depths and the mean depth of propagation. Remotely sensed data were used to assess the contribution (in the spatial structure of the geosystem) of algal vegetation on the littoral; the eelgrass fields were ranked according to the degree of projective cover. As a result of a clustering according to the similarity of spectral attributes, we identified ten groups of pixels of the image analyzed. An analysis is made of the agreement between the distribution of facies identified by expert interpretation and results of an automated classification of pixels, and the contours of landscape units were updated. The conclusion is drawn regarding integration of the computer-aided and visual approaches to interpretation of remotely sensed data for marine shallow waters leading to a “hybrid” express method of mapping landscapes of marine shallow waters.Keywords: underwater marine landscapes, monitoring, IKONOS-2, digital elevation model, interpretation, clustering of pixels.