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To better describe and understand the time dynamics in functional data analysis, it is often desirable to recover the partial derivatives of the random surface. A novel approach is proposed based on marginal functional principal component analysis to derive the representation for partial derivatives. To obtain the Karhunen-Loève expansion of the partial derivatives, an adaptive estimation is explored. Asymptotic results of the proposed estimates are established. Simulation studies show that the proposed methods perform well in finite samples. Application to the human mortality data reveals informative time dynamics in mortality rates.