In this paper, we study the effect of estimating the states of a nonlinear system when the number of states that are present on the output varies. Specifically, we examine the case of a micro-channel water tank, where we simulate scenarios of getting information from all tank levels or only some of them. Our goal is to achieve precise estimations of multiple states corresponding to the tank levels, using, in the worst case, only information from one tank. To this end, we simulate using three state estimators: the Extended Kalman Filter, the Unscented Kalman Filter, and the Particle Filter, to determine which is more accurate in scenarios with limited information. In this way, we aim to explore the possibility of reducing the number of information required in the implementation of such systems.