A new era of aerospace systems has instigated highly coupled frameworks, leading to a significant rise in design complexity. The lack of present-day design systems to govern this complexity has resulted in considerable time and schedule overruns compromising the accuracy during the development of military and commercial platforms. This work presents the framework for a new design process to reduce the complexity and improve accuracy using Spatio Temporal-based Kinematic Adaptive Sampling (ST-KAS). First, dynamic modeling of the Time Factor Matrix (TFM) and Spatial Association Matrix (SAM) based on the location and time is performed to extract relevant features. Second, the Kinematic Adaptive Frequency Sampling Algorithm is designed through a dynamic model and a Probability Uncertainty Measure. However, an adaptive control measure is required to flexibly cope with the uncertainty because the operating environment of the TFM and SAM is varied, and uncertainty exists depending on the number of locations to be analyzed for monitoring snow in aerospace applications. The performance of the Kinematic Adaptive Frequency Sampling is also verified through a numerical simulation according to computational overhead, computational time, and probability of fatality. Simulation experiments show that the suggested solution can minimize the complexity rate for sensing while maintaining the error rate at acceptable levels.