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Marine oil spills cause pollution to the environment, where timely information on the oil characteristics (i.e., oil types, concentration, thickness) is essential for spill response and post-spill assessment. Existing remote sensing models (including deep-learning or DL) have been applied to both synthetic aperture radar (SAR) and optical remote sensing images, which are primarily for presence/absence detection. Here, we propose a framework to take advantage of data richness (i.e., multi-band observations under different viewing conditions) of optical sensors to characterize oil spills beyond presence/absence detection. The framework includes a 2-step DL model (F1-score ∼ 0.88) to account for the spectral-spatial-geometrical features of optical images to achieve the presence/absence detection, and a spectral model to characterize oil type and estimate oil quantity. The framework led to a relatively comprehensive "ground truth" dataset (N = 105 oil spill cases with 1616 oil features of each being at least 3 × 3 pixels) including all possible scenarios to account for various oil types and observing conditions, which can be used in the future for model development. Application of the framework to >25,000 HY-1C/D CZI images between December 2018 and April 2023 revealed substantial oil spills in the marginal seas off China, whose spatial distributions and oil characteristics (e.g., normalized concentration, thickness, and volume) were all quantified. The DL model also appears to be applicable to other Sentinel-1 SAR as well as to other optical sensors such as Landsat Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI). The use of multi-sensor data with the proposed approach over an oil spill event in the Gulf of Mexico shows improved temporal coverage as well as better knowledge on where most of the spilled oil can be found. • A framework to use optical remote sensing to characterize oil spills is proposed. • Oil presence can be determined by a DL model and spectral-spatial-viewing variables. • Oil type and quantity are estimated using the NIR and blue bands. • An optical dataset of various types of oil features is developed. • The approach has been used to characterize oil spills in marginal seas off China. [ABSTRACT FROM AUTHOR]

Thermal remote sensing has been used in assessing oil spills in the ocean, mostly based on empirical interpretations. This study designs a ground-based experiment to measure brightness temperatures (BTs) of oil-in-water (OW) emulsions with different concentrations and oil-free water as a function of time in 32 consecutive hours. Compared with a previous thermal experiment to measure oil slicks (i.e., non-emulsified oil) with different thicknesses and considering the similarity between oil slicks and water-in-oil (WO) emulsions, it is found that (1) the diurnal response of brightness temperature difference (BTD, between oil samples and oil-free sample) to oil emulsion types (OW or WO) is similar, making it difficult to classify oil emulsion types using thermal remote sensing; (2) in contrast, BTD under thermal balance during the optimal time window appears to be a function of equivalent oil thickness (EOT) (oil volume per area, mm) regardless of oil emulsion type, suggesting that EOT could be estimated from BTD. Application of such experimental results to Landsat imagery over the Deepwater Horizon oil spill in the Gulf of Mexico suggests that although their ability to quantify oil footprint is limited, thermal data show potentials in providing unique information (e.g., estimating EOTs for up to 4 mm without the need of differentiating sub-pixel heterogeneity) to complement optical data in characterizing oil type and oil quantity when the spilled oil is thick (> 0.4 mm). • Laboratory thermal experiment conducted to characterize oil-in-water emulsions. • Oil concentrations in OW emulsions are related to brightness temperature (BT). • BT not useful to detect oil type, but useful to quantify equivalent oil thickness. • With known uncertainties, BT complements other techniques to assess oil spills. [ABSTRACT FROM AUTHOR]