In this paper, we analyze the capacity of super-resolution of one-dimensional positive sources. In particular, we consider the same setting as in [arXiv:1904.09186v2 [math.NA]] and generalize the results there to the case of super-resolving positive sources. To be more specific, we consider resolving $d$ positive point sources with $p \leqslant d$ nodes closely spaced and forming a cluster, while the rest of the nodes are well separated. Similarly to [arXiv:1904.09186v2 [math.NA]], our results show that when the noise level $ε\lesssim \mathrm{SRF}^{-2 p+1}$, where $\mathrm{SRF}=(ΩΔ)^{-1}$ with $Ω$ being the cutoff frequency and $Δ$ the minimal separation between the nodes, the minimax error rate for reconstructing the cluster nodes is of order $\frac{1}Ω \mathrm{SRF}^{2 p-2} ε$, while for recovering the corresponding amplitudes $\left\{a_j\right\}$ the rate is of order $\mathrm{SRF}^{2 p-1} ε$. For the non-cluster nodes, the corresponding minimax rates for the recovery of nodes and amplitudes are of order $\fracεΩ$ and $ε$, respectively. Our numerical experiments show that the Matrix Pencil method achieves the above optimal bounds when resolving the positive sources.