Root zone soil moisture (RZSM) refers to the amount of water present in the soil layer where plants can freely absorb water, and its information is crucial for various applications such as hydrology and agriculture. SMOS and SMAP remote sensing satellites provide soil moisture (SM) data on a global scale but limit their sensing capability to a depth of approximately 5 cm. However, for a comprehensive understanding of soil water content in the root zone, a deeper insight into RZSM is essential, which extends from 5 cm to 100 cm. Hence, to bridge the gap between the surface SM and RZSM, SMOS surface SM information (5 cm) was integrated into the root zone (100 cm) using a simple subsurface physical model. SMOS RZSM data are available on a global scale with 25 km sampling on EASE grid 2, which provides a daily temporal scale from 2010 to the present. The main aims of this study were to i) check the efficacy of a simple model-based approach and ii) investigate the importance of remote sensing SM observations for the retrieval of RZSM. Here, we investigate the benefit of the simple model-based approach by comparing SMOS RZSM (simple model) and SMAP, ERA5, and GLDAS RZSM (complex model or data-assimilation) with in-situ SM. We then investigated the role of remote sensing SM observation in the retrieval of RZSM by comparing SMOS RZSM and SMAP RZSM products over rice-irrigated areas for dry seasons (minimal rainfall) in Telangana, South India. First, SMOS RZSM was evaluated with in-situ SM data for four distinct networks: SCAN, HOBE, SMOSMANIA, and Amma catch from 2011 to 2017. The results between SMOS RZSM and in-situ SM show an average correlation coefficient between 0.54 and 0.8 with an average unbiased root mean square difference (ubRMSD) within the threshold of 0.04 m3/m3. The average correlation coefficient between the RZSM and in-situ SM for the SMOS and SMAP RZSM shows better performance in the range (0.55 to 0.93) than the ERA5 and GLDAS RZSM in the range (0.20 to 0.93). Finally, the outcomes of SMOS and SMAP RZSM over irrigated areas show that only SMOS RZSM captures changes in SM dynamics due to irrigation, particularly during the dry season.