Uncertainty quantification constructs the stochastic responses of system output under uncertainties. Traditional uncertainty quantification methods such as Monte Carlo and Full-Rank Polynomial Chaos Expansion need large amounts of samples. Compressive Sensing has the advantages of requiring few observation samples and sparse reconstruction. By combining Compressive Sensing with uncertainty quantification, the samples in demand can be reduced. Nevertheless, the sparse reconstruction algorithms directly affect predicting accuracy, especially in a practical aerodynamic scenario. To compare the effect of two l0-minimization based greedy reconstruction algorithms, Orthogonal Matching Pursuit and Subspace Pursuit, this study adopts them into uncertainty quantification of RAE2822 airfoil considering geometrical uncertainties. The Polynomial Chaos Expansion is used to realize sparse representation and Structured Random Observation Matrix is constructed by randomly selecting from Gaussian quadrature set. The two greedy reconstruction methods are compared to Monte Carlo and Full-Rank Probabilistic Collocations on convergence, required samples and accuracy. The results show that both methods could achieve similar accuracy as Monte Carlo with much fewer samples. Besides, Orthogonal Matching Pursuit is able to precisely reconstruct original signal with proper sparsity estimation, while Subspace Pursuit provides more robust prediction for different cases. This study presents an efficient approach that integrates compressive sensing into uncertainty quantification of airfoils.