Accurate monitoring of crop phenology is vital for enhancing agricultural management and understanding the impacts of climate change on crop growth cycles. This study presents a data analytic framework for knowledge management on corn phenology under climate change using remotely sensed, ground truth and meteorological data. The framework is able to detect key phenological stages in corn vegetation, such as Start of Season (SOS), Peak of Season (POS), and End of Season (EOS), using remotely sensed Normalized Difference Vegetation Index (NDVI) data. The framework supports a comprehensive trend analysis of the detected stages and comparing them with corresponding ground-based phenological data. Additionally, it allows to analyze meteorological variables, including temperature and precipitation, to assess long-term trends and potential correlations with observed phenological changes. Our findings demonstrate a strong correlation between NDVI-derived phenological stages and ground-based observations, validating the effectiveness of remote sensing for phenological monitoring. The trend analysis reveals significant shifts in SOS, POS, and EOS over the past two decades, indicating the influence of climate change on corn development cycles. These results underscore the importance of integrating remote sensing with ground-based and meteorological data to accurately track and understand phenological trends. This integration offers valuable insights into the potential impacts of climate change on future agricultural productivity and supports the development of adaptive crop management strategies.