Current agricultural models require weather forcings such as precipitation, temperature, and solar radiation along with soil, crop genetics, and field management information to estimate crop development parameters and yield. Crop growth models provide detailed formulations to simulate crop development, but their representation of hydrologic processes is less rigorous. Conversely, hydrology models are generally deficient in the simulation of crop development and farm management. An Agriculture Digital Twin – Agriculture Land Information System (AgLIS) was developed by coupling NASA’s Land Information System (LIS) with Decision Support System for Agrotechnology Transfer (DSSAT) model to estimate crop growth stages, biomass, and crop yield informed by detailed characterization of land surface and hydrology conditions. The coupled model framework leverages LIS’s capabilities of fine scale land surface modeling and assimilating remotely sensed data such as soil moisture and Leaf Area Index (LAI) to improve the model simulations. The model simulations demonstrate that soil moisture plays a critical role for crop yield estimates, capturing the effects of wet and dry conditions during the growing seasons. LAI is able to capture the vegetation growth and the impact of environmental conditions. This research thus highlights the unprecedented capability of a coupled hydrology-agriculture crop modeling framework in understanding the response of crops in synergy with meteorological and hydrological constraints.