Effective management and preservation of road assets at the network scale requires mapping and monitoring network conditions such as to enable timely targeting of maintenance interventions. However, mapping and monitoring currently tends to rely on ground-level data collection methodologies that are labourious and involve expensive specialized equipment, which makes it only possible to capture portions of road networks yearly or over longer time intervals especially in resource-constrained environments like developing countries. Thus often, the most critical maintenance needs are persistently unaccounted for leading to costly rehabilitation requirements. Remote sensing offers to improve road network mapping and monitoring for better management and has gained traction in the last decade. It is envisaged that the wide area monitoring capabilities of artificial satellites together with advancements in their technologies such as to improve spatial and temporal resolutions will increasingly be extracted for early and accurate pavement quality evaluation. Hence more studies about relationships between satellite imagery and road condition information will emerge. This study explores whether meaningful relationships can be derived between multispectral data taken by the Copernicus Sentinel-2 satellites and international roughness index (IRI) data collected on the Kenya road pavement network. Latitude-longitude coordinates for a sample of the network road sections are used to extract the Sentinel-2 data on the Google Earth Engine platform. Statistical analyses are then applied to explore data properties that significantly relate to continuous IRI data as well as ranks of IRIs representing classes of pavement conditions. From these analyses, prediction models are examined. It is expected that the study findings will lend credence to Copernicus Sentinel-2 as a useful tool for road pavement IRI mapping, including filling in data gaps. This is significant because Sentinel 2 constellation databases are publicly available on the Google Earth Engine data catalog and can therefore be cost-effectively incorporated into pavement maintenance management systems.