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Spectral indices are of fundamental importance in providing insights into the distinctive characteristics of oil spills, constituting them indispensable tools for effective action planning. The Normalized Difference Oil Index (NDOI) is a reliable metric and suitable for the detection of coastal oil spills, effectively leveraging the visible and near-infrared (VNIR) spectral bands offered by commercial sensors. The present study explores the calculation of NDOI with a primary focus on leveraging remotely sensed imagery with rich spectral data. This undertaking necessitates a robust infrastructure to handle and process large datasets, thereby demanding significant memory resources and ensuring scalability. To overcome these challenges, a novel cloud-based approach is proposed in this study to conduct the distributed implementation of the NDOI calculation. This approach offers an accessible and intuitive solution, empowering developers to harness the benefits of cloud platforms. The evaluation of the proposal is conducted by assessing its performance using the scene acquired by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor during the 2010 oil rig disaster in the Gulf of Mexico. The catastrophic nature of the event and the subsequent challenges underscore the importance of remote sensing (RS) in facilitating decision making processes. In this context, cloud-based approaches have emerged as a prominent technological advancement in RS field. The experimental results demonstrate noteworthy performance by the proposed cloud-based approach and paves the path for future research for fast-decision making applications in scalable environments. Code:  https://github.com/mhaut/scalable-ndoi

Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection