This paper examines the efficacy of merging multi-sourced enter records into time series algorithms for analyzing hyperspectral imagery. Combining entered information from different resources (e.g., from Radar, Landsat, and LiDAR) with usually used time collection algorithms (e.g., SVM, Random Forests, and Gradient Boosting) is investigated in this study. First, Landsat, LiDAR, and Radar data are purified, and the derived pixels are stacked and rasterized to generate input datasets as collections of vectors. Secondly, the entered datasets are transformed into temporal capabilities with the same old Deviation technique (SDM) and minimum Spanning Tree (MST) to visualize the records from a temporal angle. The temporal features enter the various time series algorithms for hyper hyperspectral type. The paper concludes by showing the capacity improvements because of merging more than one asset within the analyzed algorithms and further discussing feasible explorative programs.