scholarly journals Modelling Remote Sensing Reflectance to Detect Dispersed Oil at Sea

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 863 ◽  
Author(s):  
Emilia Baszanowska ◽  
Zbigniew Otremba ◽  
Jacek Piskozub
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Spectral Index ◽  
Optical Parameters ◽  
Scattering Coefficients ◽  
Wind Speeds ◽  
Remote Sensing Reflectance ◽  
Dispersed Oil ◽  
The Monte Carlo Method ◽  
Oil In Water

This paper presents a model of upwelling radiation above the seawater surface in the event of a threat of dispersed oil. The Monte Carlo method was used to simulate a large number of solar photons in the water, eventually obtaining values of remote sensing reflectance (Rrs). Analyses were performed for the optical properties of seawater characteristic for the Gulf of Gdańsk (southern Baltic Sea). The case of seawater contaminated by dispersed oil at a concentration of 10 ppm was also discussed for different wind speeds. Two types of oils with extremely different optical properties (refraction and absorption coefficients) were taken into account for consideration. The optical properties (absorption and scattering coefficients and angular light scattering distribution) of the oil-in-water dispersion system were determined using the Mie theory. The spectral index for oil detection in seawater for different wind conditions was determined based on the results obtained for reflectance at selected wavelengths in the range 412–676 nm. The determined spectral index for seawater free of oil achieves higher values for seawater contaminated by oil. The analysis of the values of the spectral indices calculated for 28 combinations of wavelengths was used to identify the most universal spectral index of Rrs for 555 nm/440 nm for dispersed oil detection using any optical parameters.

scholarly journals Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3975
Author(s):  
Bonyad Ahmadi ◽  
Mehdi Gholamalifard ◽  
Tiit Kutser ◽  
Stefano Vignudelli ◽  
Andrey Kostianoy
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Attenuation Coefficient ◽  
Temporal Variability ◽  
Caspian Sea ◽  
Ocean Color ◽  
Time Lags ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Southern Caspian Sea

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

scholarly journals Modelling a Spectral Index to Detect Dispersed Oil in a Seawater Column Depending on the Viewing Angle: Gulf of Gdańsk Case Study

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5352
Author(s):  
Emilia Baszanowska ◽  
Zbigniew Otremba ◽  
Jacek Piskozub
Keyword(s):  
Optical Properties ◽  
Spectral Index ◽  
Sea Surface ◽  
Viewing Angle ◽  
Gulf Of Gdańsk ◽  
Water Emulsion ◽  
Dispersed Oil ◽  
Oil Concentration ◽  
Gulf Of Gdansk ◽  
Oil In Water Emulsion

This paper analyzes the digital modelling of radiance reflectance of the sea surface when the water column is polluted by oil-in-water emulsion. A method tracking the fate of two billion virtual solar photons was applied to obtain the angular distribution of bottom-up radiance for a plane of sunlight striking the sea surface. For the calculations, the inherent optical properties of seawater characteristic for the Gulf of Gdańsk (southern Baltic Sea) were used. The analyses were performed for two types of oils with extremely different optical properties for an oil concentration of 10 ppm and for a roughened sea surface with a wind speed of 2 m/s. The spectral index for oil detection in seawater for different viewing angles was determined based on the results obtained for reflectance at eight wavelengths in the range of 412–676 nm for viewing angle in the range from 80° to 0°, both on the side of incidence of direct sunlight and on the opposite side. The resulting calculated spectral indexes for different wavelength combinations indicated significant dependence on the viewing angle.

scholarly journals Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

2019 ◽  
Vol 11 (2) ◽  
pp. 184 ◽  
Author(s):  
Kun Xue ◽  
Ronghua Ma ◽  
Dian Wang ◽  
Ming Shen
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Lake Taihu ◽  
Optical Parameters ◽  
Water Type ◽  
Water Types ◽  
Remote Sensing Reflectance ◽  
Inorganic Matter ◽  
Suspended Particulate ◽  
Huai River

Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing reflectance spectra (NRrs(λ)) using the k-means clustering approach, and were identified in the Sentinel 3A OLCI (Ocean Land Color Instrument) image data over lakes in the LYHR Basin. The results showed that 1) Each OWT is associated with different bio-optical properties, such as the concentration of chlorophyll-a (Chla), suspended particulate matter (SPM), proportion of suspended particulate inorganic matter (SPIM), and absorption coefficient of each component. One optical water type showed an obvious characteristic with a high contribution of mineral particles, while one type was mostly determined by a high content of phytoplankton. The other types belonged to the optically mixed water types. 2) Class-specific Chla inversion algorithms performed better for all water types, except type 4, compared to the overall dataset. In addition, class-specific inversion algorithms for estimating the Chla-specific absorption coefficient of phytoplankton at 443 nm (a*ph(443)) were developed based on the relationship between a*ph(443) and Chla of each OWT. The spatial variations in the class-specific model-derived a*ph(443) values were illustrated for 2 March 2017, and 24 October 2017. 3) The dominant water type and the Shannon index (H) were used to characterize the optical variability or similarity of the lakes in the LYHR Basin using cloud-free OLCI images in 2017. A high optical variation was located in the western and southern parts of Lake Taihu, the southern part of Lake Hongze, Lake Chaohu, and several small lakes near the Yangtze River, while the northern part of Lake Hongze had a low optical diversity. This work demonstrates the potential and necessity of optical classification in estimating bio-optical parameters using class-specific inversion algorithms and monitoring of the optical variations in optically complex and dynamic lake waters.

Remote sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements

1997 ◽  
Author(s):  
Zhongping Lee ◽  
Kendall L. Carder ◽  
Robert G. Steward ◽  
Thomas G. Peacock ◽  
Curtiss O. Davis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Inherent Optical Properties ◽  
Remote Sensing Reflectance

scholarly journals Influence of Dispersed Oil on the Remote Sensing Reflectance—Field Experiment in the Baltic Sea

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5733
Author(s):  
Kamila Haule ◽  
Henryk Toczek ◽  
Karolina Borzycka ◽  
Mirosław Darecki
Keyword(s):  
Remote Sensing ◽  
Field Experiment ◽  
Baltic Sea ◽  
Oil Spills ◽  
Light Flux ◽  
Natural Seawater ◽  
Oil Droplets ◽  
Maximal Increase ◽  
Remote Sensing Reflectance ◽  
Dispersed Oil

Remote sensing techniques currently used to detect oil spills have not yet demonstrated their applicability to dispersed forms of oil. However, oil droplets dispersed in seawater are known to modify the local optical properties and, consequently, the upwelling light flux. Theoretically possible, passive remote detection of oil droplets was never tested in the offshore conditions. This study presents a field experiment which demonstrates the capability of commercially available sensors to detect significant changes in the remote sensing reflectance Rrs of seawater polluted by six types of dispersed oils (two crude oils, cylinder lubricant, biodiesel, and two marine gear lubricants). The experiment was based on the comparison of the upwelling radiance Lu measured in a transparent tank floating in full immersion in seawater in the Southern Baltic Sea. The tank was first filled with natural seawater and then polluted by dispersed oils in five consecutive concentrations of 1–15 ppm. After addition of dispersed oils, spectra of Rrs noticeably increased and the maximal increase varied from 40% to over three-fold at the highest oil droplet concentration. Moreover, the most affected Rrs band ratios and band differences were analyzed and are discussed in the context of future construction of algorithms for dispersed oil detection.

scholarly journals Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

2018 ◽  
Vol 10 (10) ◽  
pp. 1655 ◽  
Author(s):  
Nariane Bernardo ◽  
Enner Alcântara ◽  
Fernanda Watanabe ◽  
Thanan Rodrigues ◽  
Alisson Carmo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Wind Speed ◽  
In Situ Measurements ◽  
Colored Dissolved Organic Matter ◽  
The Third ◽  
Sensing Applications ◽  
Remote Sensing Reflectance ◽  
Remote Sensing Applications

The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (ρ) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s−1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral ρ achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors.

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