A novel method for the inference of spatiotemporal decomposition of oceanic variability is presented and its performance assessed in a synthetic idealized configuration. The method is designed here to ingest velocity observation. The abilities of networks of reduced number of surface drifters and moorings at inferring spatiotemporal scales of ocean variability are quantified and contrasted. The sensitivities of inference performances for both types of platforms to the number of observation, geometrical configurations, flow regimes are presented. Because they simultaneously sample spatial and temporal variability, drifters are shown to be able to capture both spatial and temporal flow properties even when deployed in isolation. Moorings are particularly adequate for the characterization of the flow temporal variability, and may also capture spatial scales provided they are multiplied and the financial and environmental costs of associated deployments can be assumed. We show in particular that the method correctly identifies whether drifters are sampling preferentially spatial vs temporal variability. This method opens novel avenues for the analysis of existing datasets as well as the design of future experimental campaigns targeting the characterization of small scale (e.g. <100 km) Ocean variability.