Abstract
Monitoring oil spills on coastal beaches using satellite imagery has received limited attention, primarily due to the lack of characteristic spectral data as well as constraints in spatial or temporal resolution. In this study, we employ both reflectance spectroscopy and CMOS-sensing imagery to detect and characterize different species of oil contaminants on sandy beaches and investigate their behavior throughout the weathering process. Laboratory and field measurements were conducted on oil-contaminated and clean beach samples with a high-resolution portable spectrometer and a highly sensitive CMOS camera. Predictive modeling of the reflectance spectra using LW-PLS, SVR, and SVM yielded R2 values of 0.86 for oil concentration and 0.89 for weathering time, and achieved an oil species classification accuracy of 0.86. Furthermore, beach oil spills in the image dataset were detected using a DeepLabV3+ segmentation model with a ResNet-50 backbone, achieving a mean prediction accuracy of 98.73%. Finally, the segmentation model was successfully applied to accurately detect oil spill pollution on the beaches of Goa, India, confirming its field effectiveness. These reflectance spectroscopy and CMOS-sensing imagery technologies can provide critical data for calibrating remote sensing satellites, thereby offering direct technical support for targeted oil spill cleanup operations on beaches.