Soil moisture and temperature are key inputs to several precision agricultural applications such as irrigation scheduling, identifying crop health, pest and disease prediction, yield and acreage estimation, etc.  The existing remote sensing satellites based soil moisture products such as SMAP are of coarse resolution and physics based land surface model such as NLDAS, GLDAS are of coarse resolution as well as not available for real time applications.  Keeping this in focus, we are developing a soil moisture and temperature map for India using high resolution land data assimilation system (HRLDAS) as a computing tool.  The service is aimed to provide soil moisture and soil temperature at 1 km spatial resolution in near real-time (few hours’ latency) at four soil depths and vegetation root zones.  The major highlights in the development of the service are: (1) use of Global Data Assimilation System (GDAS) dataset for dynamic forcing fields, (2) ability to ingest local information about the soil characteristics (3) high resolution USGS land-cover and other static datasets, amongst others.  In this paper, we will focus on modelling set-up details and model evaluation.  Model evaluation is performed against SMAP soil moisture data and local sensors observations using conventional statistics such as MAE, RMSE and correlation coefficient.  The results clearly demonstrate the value of our service in comparison to exiting SMAP data.  In summary, the high resolution soil moisture and soil temperature service that we have developed could be used effectively in a real-time decision support system in precision agriculture.