One of the most important factors that must be understood on the management of coastal area is the distribution of benthic habitat. Benthic habitat is an economically and ecologically important natural resource in coastal area. The distribution of benthic habitat can be well-presented using map. Benthic habitat map is a powerful tool for coastal management planning such as locating protected area, prediction species occurrence, evaluation of management effect and also biodiversity assessment. One of the cost-effective methods to provide such information with fast, high repetition and accurate result is using satellite image to map those resources in combination with field observation. Remote sensing technology allows us to produce good habitat maps. The problem is thehabitats are located submerged and limit the ability of remote sensing data to map the habitat. The aims of this study is to inverse the submerged reflectance into wet reflectance using integration of concepts on how pixels value over benthic habitat is recorded by sensor, water column attenuation, and bathymetry data. The accuracy of the integrated model will be compared with the accuracy of the existing model on each classification scheme. Last purpose is to combine the integrated model with PCA to get more detailed information on benthic habitat types. The hierarchical benthic habitat classifications were derived from ecological basis and habitat spectral separability analysis. The purpose of using hierarchical scheme is to cover all the possible existing habitat and to cover different management needs. The methods used in the study were conversion into surface reflectance, sunglint removal, water column and bathymetry generation, water depth invariant index, PCA (Principle Component Analysis) transformation and integrated model. Digital classification process was carried out using maximum likelihood with knowledge-based image segmentation. The result shows that the integrated model could inverse the submerged reflectance into wet reflectance but produced slightly lower accuracy compared to Lyzenga or PCA on each habitat classification scheme due to discrete zoning in bathymetry data. In classifying 5, 7, and 13 habitat class, Lyzenga was the most accurate with 79.59%, 74.73% and 37.89% accuracy respectively, PCA produced 82.65%, 71.57% and 37.89% respectively, and the integrated model produced 73.46%, 69.47% and 37.89% accuracy respectively. The combination of integrated model with PCA produced the most accurate result on the detailed classification scheme with average accuracy of 61.56% and overall accuracy 50%. The integrated model competes well with other methods on each classification scheme, especially on detailed classification scheme.