This paper focuses on the optimal sensor placement problem with the purpose of signal extraction in an underdetermined noisy setting. Assuming prior information on the spatial gain of the measured signal and on the spatial noise correlation, we propose a sensor placement criterion based on the maximization of the average signal to noise ratio of the estimated signal. Our approach differs from classical Kriging based optimal sensor placement approaches, since the latter focus on best reconstruction of the spatial measured field and not on the estimation of an underlying signal. Performance analysis of the proposed criterion is presented along with synthetic results. It is observed that sensor placement with our criterion significantly outperforms other methods based on Kriging.