Large-scale spatiotemporal photonic reservoir computer (RC) systems offer remarkable solutions for massively parallel processing of a wide variety of hard real-world tasks. In such systems, neural networks are created by either optical or electronic coupling. Here, we investigate the impact of the optical coherence on the performance of large-scale spatiotemporal photonic RCs by comparing a coherent (optical coupling between the reservoir nodes) and incoherent (digital coupling between the reservoir nodes) RC systems. Although the coherent configuration offers significant reduction on the computational load compared to the incoherent architecture, for image and video classification benchmark tasks, it is found that the incoherent RC configuration outperforms the coherent configuration. Moreover, the incoherent configuration is found to exhibit a larger memory capacity than the coherent scheme. Our results pave the way towards the optimization of implementation of large-scale RC systems.