Abstract Water in oil (WO) and oil in water (OW) emulsions from marine oil spills have different physical properties, volume concentrations, and spectral characteristics. Identification and quantification of these different types of oil emulsions are important for oil spill response and post-spill assessment. While the spectral characteristics of WO and OW emulsions have been presented in previous studies including Part I of this series, their application to airborne and satellite imagery is further demonstrated here. Using AVIRIS and Landsat observations, we firstly show that false color Red-Green-Blue composite images from Landsat-like sensors (R: 1677 nm, G: 839 nm, B: 660 nm) are effective in differentiating WO and OW emulsions as they show reddish and greenish colors, respectively, in such composite images. This is a consequence of the relative difference in the reflectance of WO and OW emulsions at 1677 and 839 nm, which is not impacted by the presence of medium-strength sunglint or the surface heterogeneity within medium-resolution pixels (e.g., 30 m). Based on image statistics, a decision tree method is proposed to classify oil type, and oil quantification is further attempted, with results partially validated through spectral analysis and spatial coherence test. The numerical mixing experiments using AVIRIS pixels further indicate that the SWIR bands might be used to develop linear unmixing models in the future once the coarse-resolution oiled pixels are first classified to WO and OW types, and 1295 nm is the optimal wavelength to perform spectral unmixing of mixed coarse-resolution pixels.

中文翻译：

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海洋中石油乳剂的光学解释——第二部分：多波段粗分辨率图像的应用

摘要 海洋溢油产生的油包水（WO）和水包油（OW）乳液具有不同的物理性质、体积浓度和光谱特征。识别和量化这些不同类型的油乳液对于溢油响应和溢油后评估很重要。虽然 WO 和 OW 乳剂的光谱特性已在包括本系列的第 I 部分在内的先前研究中介绍过，但它们在机载和卫星图像中的应用在这里得到进一步证明。使用 AVIRIS 和 Landsat 观测，我们首先表明来自类 Landsat 传感器（R：1677 nm，G：839 nm，B：660 nm）的假彩色红绿蓝复合图像可有效区分 WO 和 OW 乳剂，因为它们在这样的合成图像中分别显示红色和绿色。这是 WO 和 OW 乳剂在 1677 和 839 nm 处反射率相对差异的结果，这不受中等强度太阳光的存在或中等分辨率像素（例如 30 m）内的表面异质性的影响。提出了基于图像统计的决策树方法对油类进行分类，并进一步尝试了油类量化，通过光谱分析和空间相干性测试对结果进行了部分验证。使用 AVIRIS 像素的数值混合实验进一步表明，一旦粗分辨率上油像素首先被分类为 WO 和 OW 类型，SWIR 波段可能会在未来用于开发线性分离模型，并且 1295 nm 是执行光谱的最佳波长混合粗分辨率像素的分离。