## 1 Introduction
Whilst this report has been significant as an advocacy tool for affected communities and a benchmark resource to raise awareness among policymakers, such reports have limitations, including restricted spatial coverage, sampling bias due to site accessibility, and narrow temporal scope. Efforts to more precisely quantify the full extent of the effects of petroleum contamination have been hindered by the cost associated with comprehensive field-based surveys. Traditional field surveys are costly, time-consuming, and largely unsuitable for continuous monitoring, which greatly restricts understanding of the full-scale impact of oil spills. Leveraging remote sensing from low Earth orbit satellites offers a promising avenue to overcome these limitations and enhance assessment, by simultaneously identifying and characterising visible changes in biomass, infrastructure, and local natural resources coincident with reported oil spillage locations, particularly given the spatiotemporal coverage available. This is of particular relevance to the Niger Delta region, where the clearest proxy for localised ecosystem health on the ground is the status of its mangrove forests. Remotely sensing any given location's biomass status using satellites is traditionally enabled by the use of optically derived vegetative health indices, which capture a range of biophysical parameters. Prominent examples include the Normalized Difference Vegetation Index (NDVI) and the Leaf Area Index (LAI), both of which have seen increased accessibility in recent years, owing to the emergence of extensive catalogues featuring analysis-ready Earth Observation (EO) data products [20]. However, it is crucial to acknowledge the inherent limitations of passive optical sensors, namely their vulnerability to cloud cover and illumination variations, which are persistent issues in tropical regions such as the Niger Delta. For Sentinel-2 optical data over our region of interest between 2016 and 2023, there were only a total of 369 available scenes where the average cloud cover was 72% and only 22 scenes had a cloudy pixel percentage of less than 20%. Having access to such a limited number of adequate-quality scenes underscores the issue of sampling different stages of the phenology year-on-year. This challenge renders monitoring via passive optical sensors unreliable with sizable temporal gaps between measurements. The advent of open-access active sensors, such as Sentinel-1, presents an alternative solution. In particular, synthetic aperture radar (SAR) offers insights into target structure via backscatter intensity and polarisation, where the timeliness and reliability of the observations are facilitated by its cloud-penetrating, all-weather, and illumination-independent sensing capabilities [21]. This study utilises the timeliness and reliability of Sentinel-1, in conjunction with machine learning classification techniques, to establish a novel and powerful workflow that can identify zones within the mangrove forests of the Niger Delta that are highly impacted by oil spillage events. We specifically focus on highly polluted areas to assess and quantify the extent of mangrove damage over time in proximity to known pipeline infrastructures and spillages.