Remote Sensing-Based Monitoring of Road Degradation Near Oil Fields in Block 3, South Sudan

Road infrastructure supporting oil extraction in South Sudan's Block 3 oilfield (Upper Nile State) deteriorates rapidly under the combined stresses of heavy oil tanker and equipment traffic, expansive Vertisol clay subgrades, and extreme seasonal flooding, yet systematic monitoring is absent due to the region's remoteness, active security constraints, and absence of roadside instrumentation. This study presents the first multi-temporal, remote sensing-based road degradation monitoring assessment for Block 3, South Sudan, using dense Sentinel-2 multispectral imagery time series (2017–2023, n = 184 scenes) and Sentinel-1 C-band synthetic aperture radar (SAR) backscatter data. A Road Degradation Index (RDI) is derived from a combination of road-corridor NDVI suppression, bare soil index (BSI), SWIR ratio, and SAR backscatter, validated against 60 field-surveyed road condition points (International Roughness Index, IRI, and visual distress assessment). The resulting Random Forest (RF) classification model achieves an overall accuracy of 87.6% and kappa coefficient of 0.87 against independent field reference data. Analysis of the 2017–2023 time series reveals that 24% of the 412 km assessed road network (Category A primary oilfield access roads) has deteriorated to Severe degradation (RDI < 0.35), compared to only 8% in 2019, representing a 200% increase in severely degraded network length in four years. Seasonal analysis confirms that wet-season RDI values are 31–44% lower than dry-season values for the same segments, with Category A segments showing the steepest wet-dry RDI gradient, indicating acute flood-induced softening and rutting cycles. The study establishes a validated, cost-effective, and operationally scalable satellite monitoring protocol for oil corridor road n