Yingcheng Lu

Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China

Abstract Determining oil slick thickness plays an important role in assessing oil spill volume and its environmental impacts on the ocean. In this study, we used a Hyperion image of an oil spill accident area and seawater and fresh crude oil samples collected in the Bohai Sea of China. A well-controlled laboratory experiment was designed to simulate spectral responses to different oil slick thicknesses. Spectral resampling and normalization methods were used to reduce the differences in spectral reflectances between the experimental background seawater sample and real background seawater. Fitting the analysis with laboratory experimental data results showed a linear relationship between normalized oil slick reflectance and normalized oil slick thickness [20th band (R 2=0.92938, n=49, p<0.01), 26th band (R 2=0.93806, n=49, p<0.01), 29th band (R 2=0.93288, n=49, p<0.01)]. By using these statistical models, we successfully determined the normalized oil slick thickness with the Hyperion image. Our results indicate that hyperspectral remote sensing technology is an effective method to monitor oil spills on water. The spectral ranges of visible green and red light were the optimal bands for estimating oil slick thickness in case 2 water. The high, stabilized spectral reflectance of background seawater will be helpful in oil slick thickness inversion.

An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory

Oil slick thickness was an important parameter for estimating oil spill volume. Two-beam interference theory could be used to interpret the behavior of reflected and refracted light in oil slick. A quantitative relationship between thickness and spectral reflectance of oil slick could be established based on this theory. Some parameters have the properties of numerical oscillation and can be ignored in practical application. In addition, numerical approximation results showed that two parameters of the relationship were closely related to the spectral reflectance of background water and the thick oil slick. Therefore, a practical model for estimating oil slick thickness could be derived and proved to be consisted with theoretical relationship.

Progress in Marine Oil Spill Optical Remote Sensing: Detected Targets, Spectral Response Characteristics, and Theories

Different oil spill pollution types could be produced in oil transport and weathering processes. Investigation of these pollution types is beneficial for oil spill recovery and processing. Optical remote sensing techniques play an important role in marine oil spill monitoring and have the ability to identify different oil spill pollution types. Recently, research on oil spill optical remote sensing has made much progress in detecting targets, identifying spectral response characteristics, and formulating theories. Floating black oil, oil slicks, and oil-water mixture in marine oil spill accidents are the main targets to be investigated by optical remote sensors. The visible spectral response differences of these targets are the base of oil spill optical remote sensing research. Bi-directional reflectance distribution function, light interference, absorption, and scattering of targets produce different spectra. Therefore, oil spill optical remote sensing could be used to identify the main oil spill pollution types and estimate oil spill volume.

Refinement of the critical angle calculation for the contrast reversal of oil slicks under sunglint

It has long been observed that oil slicks under sunglint can reverse their optical contrast against nearby oil-free seawater. Such a phenomenon has been described through both empirical statistical analysis of the sunglint strength and modeled theoretically using a critical angle concept. The critical angle, in this model, is the angle at which the image pixels show no or negligible contrast between oiled and nonoiled seawater. Pixels away from this critical angle show either positive or negative contrast from the oil-free pixels. Although this concept has been fully demonstrated in the published literature, its calculation needs to be further refined to take into account: (1) the different refractive indices of oil slicks (from natural seeps) and seawater and (2) atmospheric effects in the sensor-measured radiance. Using measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) over oil films in the Gulf of Mexico, we show improvement in the modeled and MODIS-derived reflectance over oil slicks originated from natural seeps after incorporating these two factors in the model. Specifically, agreement between modeled and measured sunglint reflectance is found for both negative and positive-contrasting oil slicks. These results indicate that surface roughness and reflectance from oil films can be estimated given any solar/viewing geometry and surface wind. Further, this model might be used to correct the sunglint effect on thick oil under similar illumination conditions. Once proven possible, it may allow existing laboratory-based models, which estimate oil thickness after such corrections, to be applied to remote sensing imagery.

Analyzing the effects of particle size on remotely sensed spectra: a study on optical properties and spectral similarity scale of suspended particulate matters in water

Particle size is an important factor for determining the concentration of suspended particle matter (SPM) in water using optical remotely sensed data. We collected reflectance spectra of five SPM samples with different particle sizes in a controlled laboratory experiment using a spectroradiometer. The theoretical relationship between particle size distributions and backscattering coefficient was deduced based on a spectral reflectance model. The backscattering coefficient of the complete SPM sample can be computed using the linear weighted combination of four percentages of different subsamples. The spectral similarity scale results indicate the optimal optical bands and boundary conditions for particle size and concentration of SPM remote sensing. The particle size can be evaluated by optical remote sensing to improve the applicability and precision of remote sensing models for SPM concentration inversion.

Paths correlation matrix.

Both the Jones and Mueller matrices encounter difficulties when physically modeling mixed materials or rough surfaces due to the complexity of light-matter interactions. To address these issues, we derived a matrix called the paths correlation matrix (PCM), which is a probabilistic mixture of Jones matrices of every light propagation path. Because PCM is related to actual light propagation paths, it is well suited for physical modeling. Experiments were performed, and the reflection PCM of a mixture of polypropylene and graphite was measured. The PCM of the mixed sample was accurately decomposed into pure polypropylenes single reflection, pure graphites single reflection, and depolarization caused by multiple reflections, which is consistent with the theoretical derivation. Reflection parameters of rough surface can be calculated from PCM decomposition, and the results fit well with the theoretical calculations provided by the Fresnel equations. These theoretical and experimental analyses verify that PCM is an efficient way to physically model light-matter interactions.

Thermal Infrared Contrast Between Different Types of Oil Slicks on Top of Water Bodies

Thermal remote sensing is an effective technique for marine oil slick detection. However, many factors, such as the oil type, slick thickness, sensor capability, and the background environment, can together have an impact on the remotely sensed thermal imagery. These cross-coupling effects can usually be clarified by ground-based experiments. In this letter, four different types of oil slicks on water bodies were prepared and their brightness temperatures (BTs) measured periodically in an outdoor experiment. The results indicated that there are obvious differences in the BTs between the different types of oil, especially between crude and refined oil. Defined BT time-changing contrast coefficient of different type of oil slicks numerically displays these significant difference in different observed periods. These results imply that thermal sensors may be used to discern the type of oil slick and that time series of thermal observations will be able to help with oil-type detection in the future. Moreover, the optimal strategy is to make a series of observations covering the cooling period from noon (the optimal detection time) to around sunset.

Chlorophyll-a concentration estimated by hyperspectral remote sensing in Liaodong Bay

The research was intended to estimate Chl-a concentration of coastal water in Liaodong Bay, China based on reflectance spectra data collected in situ and satellite hyperspectral data—Hyperion image. After processing and atmospheric correction, the reflectance of water extracted from Hyperion image can be used to express the spectral characteristics of different Chl-a concentration. Ratio calculation of reflectance between absorption and reflection peaks of Chl-a, the derivative analysis of spectrum can greatly improve the correlation with Chl-a concentration. Exponential model of Chl-a concentration with variable of band ratio between 681nm and 671nm was applied to Hyperion and the mean absolute percent error is 34% and root mean square error value is 3.30μgl-1.

Using remote sensing to detect the polarized sunglint reflected from oil slicks beyond the critical angle

The critical angle at which the brightness of oil slicks and oil-free seawater is reversed occurs under sunglint and is often shown as an area of uncertainty due to different roughness and surface Fresnel reflection parameters. Consequently, differentiating oil slicks from the seawater in these areas using optical sensors is a challenge. Polarized optical remote sensing techniques provide complementary information for intensity imagery with different physical properties and, thus, possess the ability to resolve this difficult problem. In the polarized reflectance model, the degree of linear polarization (DOLP) of sunglint depends on accurately knowing the Stokes parameter for the reflected light, and varies with the refractive index of the surface layer and the viewing angles. For the polarized detection of oil slicks, the highest sensitivity of the DOLP to the refractive index is located within the specular reflection direction where the sum of the solar and sensor zenith angles is 82.6°. The modeled results clearly indicate that the DOLP of oil slicks is weaker in comparison with oil-free seawater under sunglint. Using measurements from the space-borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) over the Deepwater Horizon oil spill in the Gulf of Mexico, we illustrate that the PARASOL-derived DOLP difference between the oil spill and seawater is obvious and is in accordance with the modeled results. These preliminary results suggest that the potential of multi-angle measurement and feasibility of deriving refractive index of ocean surface from space-borne sensors need further researches.

Using optical remote sensing model to estimate oil slick thickness based on satellite image

An optical remote sensing model has been established based on two-beam interference theory to estimate marine oil slick thickness. Extinction coefficient and normalized reflectance of oil are two important parts in this model. Extinction coefficient is an important inherent optical property and will not vary with the background reflectance changed. Normalized reflectance can be used to eliminate the background differences between in situ measured spectra and remotely sensing image. Therefore, marine oil slick thickness and area can be estimated and mapped based on optical remotely sensing image and extinction coefficient.