In this dataset, we present the spectral variability of oil slicks under different observing conditions using MODIS (Moderate Resolution Imaging Spectroradiometer), MERIS (Medium Resolution Imaging Spectrometer), MISR (Multi-angle Imaging SpectroRadiometer), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and AVIRIS (Airborne Visible/ Infrared Imaging Spectrometer). Optical remote sensing is commonly used to detect oil in the surface ocean due to the spectral differences between oil and water, allowing to modulate oil–water spatial and spectral contrasts. However, understanding these contrasts is challenging because of variable results from laboratory and field experiments, as well as different observing conditions and spatial/spectral resolutions of remote sensing imagery. A multistep scheme is proposed to classify oil type (emulsion and non-emulsion) and to estimate relative oil thickness for each type based on the known optical properties of oil, with sample results from AVIRIS and MODIS imagery provided in the dataset. This dataset supports the publication: Sun, S., & Hu, C. (2018). The Challenges of Interpreting Oil-Water Spatial and Spectral Contrasts for the Estimation of Oil Thickness: Examples From Satellite and Airborne Measurements of the Deepwater Horizon Oil Spill. IEEE Transactions on Geoscience and Remote Sensing, 1–16. doi:10.1109/tgrs.2018.2876091
The dataset was generated to demonstrate oil slick spectral variability under different observing conditions in multispectral and hyperspectral remote sensing imagery, and to figure out parameters other than oil thickness that contribute to the change of oil slick reflectance in remote sensing imagery in the real marine environment. The information and results derived in this study will contribute to the development of algorithms to estimate oil thickness by using multispectral satellite remote sensing imagery.
Oil spill, Optical remote sensing, Oil thickness, Oil emulsion, hyperspectral, multispectral, MODIS, MERIS, Landsat 7, MISR, AVIRIS, Rayleigh-corrected reflectance (Rrc), sun glint strength (LGN)
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This work is licensed under a Creative Commons Public Domain Dedication 1.0 License.
Scholar Commons Citation
Hu, Chuanmin, "Spectral variability of oil slicks under different observing conditions derived from satellite and airborne optical remote sensing" (2019). C-IMAGE data. 35.