Remote sensing is rapid and effective in monitoring crop fields to provide decision support to crop production management in field planning, nutrient management, pest control, irrigation, and harvest. Multi-source, multi-scale, multi-temporal agricultural remote sensing and monitoring provides data with huge volume and high complexity for various analytical applications for effective precision agricultural operations. In the past decade, precision agricultural research have been conducted with the images acquired in the research farms over an area of 400 ha in the center of the Mississippi Delta. The images were acquired from high-resolution satellites, an agricultural airplane, and unmanned aerial vehicles along with ground-based detection and measurement. The image sensors are red-green-blue color, visible-near infrared (VNIR) multispectral, VNIR hyperspectral, and thermal infrared. The image data are not only valuable in research for precision agriculture, weed science, and crop genetics but also able to provide guides for farm consultants and producers in their digital agriculture practices in this area. The purpose of this project is to design and develop a systematic prototype to manage and publish the remote sensing image data acquired from different sources at different spatial and temporal scales on internet and mobile platforms to provide services to the local, regional, national, and even global professionals and farmers. To accommodate all data products, the images have to be resampled to fit into a global image tile structure with a data cube by stacking the image tiles in time sequences covering the same area on the ground. The application of a global image tile structure allows the local data tied into a global remote sensing big data management framework.