As a significant and cost-effective method of obtaining shallow seabed topography, satellite derived bathymetry (SDB) can acquire a wide range of shallow sea depth by integrating a small quantity of in-situ water depth data. This method is a beneficial addition to traditional bathymetric topography. The seafloor's spatial heterogeneity leads to inaccuracies in bathymetric inversion, which reduces bathymetric accuracy. By utilizing multispectral data with multidimensional features, an SDB approach incorporating spectral and spatial information of multispectral images is proposed in this study. In order to effectively increase the accuracy of bathymetry inversion throughout the entire area, first the random forest with spatial coordinates is established to control bathymetry spatial variation on a large scale. Next, the Kriging algorithm is used to interpolate bathymetry residuals, and the interpolation results are used to adjust bathymetry spatial variation on a small scale. The data from three shallow water sites are experimentally processed to validate the method. Compared with other established bathymetric inversion techniques, the experimental results show that the method effectively reduces the error in bathymetry estimation caused by spatial heterogeneity of the seabed, producing high-precision inversion bathymetry with a root mean square error of 0.78 to 1.36 meters.