This research introduces a technique, for categorizing types of land and crops through the analysis of Sentinel 2 satellite images using Time-Weighted Dynamic Time Warping (TWDTW) algorithm. The primary objectives of this research involve developing a method to gather data on crops and land surfaces from Sentinel 2 satellite images and tracking the variations, in crop growth and land usage over time. The procedure includes gathering, sorting and extracting information about crops and land usage then analyzing how these evolve over time using the TWDTW algorithm. The TWDTW method is straightforward to implement for comparing and examining patterns in crop growth and land usage data through the utilization of Python programming language and its associated modules. By establishing a cost matrix, the software computes alignment costs between Normalized difference vegetation index (NDVI) time series data patterns and reference patterns to optimize alignment paths for cost minimization. This technique ensures that pixels are categorized based on their characteristics. Using accuracy measures to check the system's usefulness and effectiveness in real-world agricultural management situations is part of evaluating its performance. When the plans are made, they show the results, which show how different types of crops have changed land use over time. These pictures give us information that helps people in the field make decisions.