Satellite data in conjunction with geoinformatics are used to study the land cover change dynamics, extraction of crop information and the monitoring of crop growth. The information derived from the satellite may contain contaminated values due to the atmospheric effects, geometric errors, snow and clouds. These contaminated values can be identified and eliminated using time series analysis to further streamline for agricultural monitoring and prognostic applications. The inherent advantages and disadvantages of existing streamlining methods limit their usability in particular situation. The method proposed in this study synergizesthe use of interpolation, running median and moving average. This has clearly shown the proposal’s capability in preserving the trend in the series in addition to streamlining the temporal profile of satellite data in the Himalayan foothills. This will make the road map for satisfactory crop growth monitoring and crop yieldestimation. Analysis based on the root mean square error and F-Test has been presented to deduce the results and interpretations.