Crop models are often fueled by (historical) yield measurements to help better predict the yields at a farm level. However to extend yield predictions to a regional or even global scale, there is a gap in the information required to calibrate such crop models. In addition, the heterogeneity of data obtained from various sources requires extensive pre-processing to provide the necessary inputs for running these crop models. Physical Analytics Integrated Data Repository and Services (PAIRS) is a geo-spatial big data platform that was used in this study to source weather, soil, and satellite data necessary for running crop models. Data curation and sensor error corrections are automatically handled by PAIRS and all data is indexed and stored in a distributed storage system for quick and parallelized access and processing. In recent years, satellite based vegetation indices have been used to derive the key phenological stages of crops and improve the accuracy of yield forecasts. In this work we have studied vegetation index changes for specific crops across multiple years to recognize crops and track the bio mass changes. Specifically winter wheat (Triticum aestivum L.) yield was simulated by coupling PAIRS with the Decision Support System for Agrotechnology Transfer (DSSAT). The model was calibrated by obtaining several phenological data derived from PAIRS. This study successfully demonstrates how to leverage big data technologies for providing the necessary data to improve the accuracy of crop yield forecasts.