Remote sensing technology allows users to acquire detailed information about the Earth’s surface on a temporal basis. Various satellites have now collected a huge quantity of data with worldwide coverage. Those commercially or freely available multi-sensor and multi-temporal data have been widely used for monitoring global earth resources, agriculture, urban development, post-flood analysis, and other purposes. Time-series analysis on a broad geographic scale necessitates a significant amount of satellite data download and processing operations, which is expensive due to the requirement for high computational power, storage, and specialized tools. Maintaining such resources can cost heavily to the research/commercial organizations. Considering the shortcomings, we have developed an automated satellite data downloading and processing pipeline on Amazon Web Service (AWS) cloud environment. AWS offers services that aid in building sophisticated applications with increased flexibility, scalability, and reliability. This pipeline uses Sentinel-2 satellite data to deliver agricultural solutions on a temporal basis at the farm level. The overall framework developed on the AWS platform is very cost-effective, optimized, and scalable.