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

AFRICAN JOURNAL OF AERIAL PHOTOGRAPHY AND REMOTE SENSING Vol. 7, No. 1, March 2026 • Pan-African Research Journals (PARJ) • Open Access • Peer-Reviewed ORIGINAL RESEARCH ARTICLE Remote Sensing-Based Monitoring of Road Degradation Near Oil Fields in Block 3, South Sudan Aduot Madit Anhiem1, Department of Civil Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia Correspondence: [email protected] • [email protected] • Received: 01 January 2026 | Accepted: 14 January 2026 | Published: 10 March 2026 DOI: 10.5281/zenodo.19234278 | Article ID: AJAPRS-2025-0089 Abstract—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 k