Vertical Soil Moisture Profile (VSMP), a key hydrological variable along with its dynamics, needs to be defined in order to understand, model and analyze the hydrological process. The estimation of VSMP plays an important role in improving the efficiency of crop yield and management along with understanding different hydro-climatological processes. Though the data acquired from the field observations are mostly accurate, however, attaining longtime and widespread data is nearly impossible. Recently, the satellite retrieved surface soil moisture data along with Hydrological Soil Groups (HSG) information have been utilized to develop a statistical model namely Statistical Soil Moisture Profile (SSMP) Model (Pal and Maity in J Hydrol 570:141–155, 2019 [1]). The same has been used to develop a vertical soil moisture profile for entire Indian mainland (Pal et al. in J Hydrol 601:126807, 2021 [2]). The developed dataset consists of numbers of missing values derived from the input surface soil moisture information. Recently, NASA has launched Soil Moisture Active Passive (SMAP) providing daily global soil moisture information at 9 km × 9 km spatial resolution and consists of no missing values. Hence, the current study aims to utilize this SMAP data to develop a vertical soil moisture profile base (at 10, 20, 51 and 102 cm depths) for the Godavari river basin in India using the SSMP model which is based on the coupling and memory approach. The spatially varying nature of soil moisture is incorporated in the study by overlaying the HSG information (data) obtained from Natural Resources Conservation Services (NRCS) database at 250 m × 250 m spatial resolution. Although, the developed dataset is devoid of missing values due to continuous availability of SMAP data, however, the study is limited by unavailability observed data leading to no or inaccurate validation. However, the generated VMSP database has immense potential in the field of hydrological modelling and agricultural practices.