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Spectral variability of oil slicks under different observing conditions derived from satellite and airborne optical remote sensingCenter for the Integrated Modeling and Analysis of Gulf Ecosystems II (C-IMAGE II)
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Suggested Citation:
Shaojie Sun; Chuanmin Hu. 2019. Spectral variability of oil slicks under different observing conditions derived from satellite and airborne optical remote sensing. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/n7-1nhg-ez10
Abstract:
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
Purpose:
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.
Theme Keywords:
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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Spectral variability of oil slicks under different observing conditions derived from satellite and airborne optical remote sensing
Identification Information
Distribution Information
Metadata Maintenance Information
Metadata:
File identifier:
R4.x267.000-0113-metadata.xml
Language:
eng; USA
Character set:
Character set code:
utf8
Hierarchy level:
Scope code:
dataset
Metadata author:
Responsible party:
Individual name:
Carla Fotherby
Organisation name:
University of South Florida
Position name:
Collections Specialist
Contact info:
Contact:
Phone:
Telephone:
Voice:
8139746220
Facsimile:
Address:
Address:
Delivery point:
4202 E. Fowler Ave.
City:
Tampa
Administrative area:
Florida
Postal code:
33620
Country:
USA
Electronic mail address:
cfotherby@usf.edu
Role:
Role code:
pointOfContact
Date stamp:
2021-05-04T20:04:56+00:00
Metadata standard name:
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
Metadata standard version:
ISO 19115-2:2009(E)
Dataset URI:
https://data.gulfresearchinitiative.org/metadata/R4.x267.000:0113
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Identification info:
Data identification:
Citation:
Citation:
Title:
Spectral variability of oil slicks under different observing conditions derived from satellite and
airborne optical remote sensing
Alternate title:
Date:
Date:
Date:
2019-01-09
Date type:
Date type code:
publication
Identifier:
Identifier:
Code:
Anchor: xlink: https://dx.doi.org/10.7266/n7-1nhg-ez10 title: DOI
doi:10.7266/n7-1nhg-ez10
Abstract:
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
Purpose:
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.
Status:
Progress code:
completed
Point of contact:
Responsible party:
Individual name:
Chuanmin Hu
Organisation name:
University of South Florida / College of Marine Science
Position name:
Professor
Contact info:
Contact:
Phone:
Telephone:
Voice:
7275533987
Facsimile:
Address:
Address:
Delivery point:
140 7th Ave South
City:
St. Petersburg
Administrative area:
Florida
Postal code:
33701
Country:
USA
Electronic mail address:
huc@usf.edu
Role:
Role code:
pointOfContact
Point of contact:
Responsible party:
Individual name:
Shaojie Sun
Organisation name:
University of South Florida / College of Marine Science
Position name:
Graduate Student
Contact info:
Contact:
Phone:
Telephone:
Voice:
8138412976
Facsimile:
Address:
Address:
Delivery point:
140 7th Ave South
City:
St. Petersburg
Administrative area:
Florida
Postal code:
33701
Country:
USA
Electronic mail address:
suns@mail.usf.edu
Role:
Role code:
principalInvestigator
Descriptive keywords:
Keywords:
Keyword:
Oil spill
Keyword:
Optical remote sensing
Keyword:
Oil thickness
Keyword:
Oil emulsion
Keyword:
hyperspectral
Keyword:
multispectral
Keyword:
MODIS
Keyword:
MERIS
Keyword:
Landsat 7
Keyword:
MISR
Keyword:
AVIRIS
Keyword:
Rayleigh-corrected reflectance (Rrc)
Keyword:
sun glint strength (LGN)
Type:
Keyword type code:
theme
Descriptive keywords:
Keywords:
Keyword:
Gulf of Mexico
Type:
Keyword type code:
place
Resource constraints: title: Cite As
Constraints:
Use limitation:
Shaojie Sun; Chuanmin Hu. 2019. Spectral variability of oil slicks under different observing conditions
derived from satellite and airborne optical remote sensing. Distributed by: Gulf of Mexico Research
Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M
University–Corpus Christi. doi:10.7266/n7-1nhg-ez10
Resource constraints: title: CC0 License
Legal constraints:
Use constraints:
Restriction code:
licenceUnrestricted
Other constraints:
This information is released under the Creative Commons license - No Rights Reserved - CC0 1.0 Universal
(https://creativecommons.org/publicdomain/zero/1.0/). The person who associated a work with this deed
has dedicated the work to the public domain by waiving all of his or her rights to the work worldwide
under copyright law, including all related and neighboring rights, to the extent allowed by law. You can
copy, modify, distribute and perform the work, even for commercial purposes, all without asking
permission.
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All materials on this website are made available to GRIIDC and in turn to you "as-is." Content may only be
submitted by an individual who represents and warrants that s/he has sufficient rights to be able to
make the content available under a CC0 waiver. There is no warranty (expressed or implied) to these
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consequential, indirect, or punitive damages that result from the use or the inability to use either
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feature, content, or terms of use, your sole and exclusive remedy is to discontinue use.
Aggregation Info:
AggregateInformation:
Aggregate Data Set Name: title: Related Publication Citation
Citation:
Title:
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
Date:
inapplicable
Aggregate Data Set Identifier: title: Related Publication DOI
Identifier:
Code:
Anchor: xlink: https://dx.doi.org/10.1109/tgrs.2018.2876091 title: DOI
doi:10.1109/tgrs.2018.2876091
Association Type:
Association type code:
crossReference
Language:
eng; USA
Topic category:
Topic category code:
oceans
Topic category:
Topic category code:
environment
Topic category:
Topic category code:
geoscientificInformation
Topic category:
Topic category code:
imageryBaseMapsEarthCover
Extent:
Extent:
Geographic element:
Geographic bounding box:
West bound longitude:
-91
East bound longitude:
-84
South bound latitude:
26
North bound latitude:
31
Geographic element:
BoundingPolygon:
Polygon:
Polygon:
Outer boundary:
Linear ring:
gml:posList:
26 -91 31 -91 31 -84 26 -84 26 -91
Temporal element:
Temporal extent:
Extent:
Time period:
Description:
ground condition
Begin date:
2010-04-24
End date:
2014-08-23
Supplemental Information:
MODIS: Four Moderate Resolution Imaging Spectroradiometer (MODIS; onboard Aqua and/or Terra)
Rayleigh-corrected reflectance (Rrc, units=dimensionless) imagery in HDF format processed with the software
SeaDAS (version 7.0) at a 250 m spatial resolution and mapped to a cylindrical equidistant projection
[bounding box (North, South, West, East; decimal degrees) = 31, 26, -91, -84] for April 26, 2010 (Aqua), May
17, 2010 (Aqua and Terra) and May 28, 2010 (Terra). HDF files contain the Rrc data at the following
wavelengths (nm): 412, 443, 469, 488, 531, 547, 555, 645, 667, 678, 748, 859, 869, 1240, 1640, 2130; the
viewing geometry [solar zenith (solz), sensor zenith (senz), solar azimuth (sola), and sensor azimuth
(sena)]; and glint coefficient (glint_coef). An RGB true-color image was created for each MODIS image. The
Rrc data was extracted at selected locations and saved in excel files (latitude (decimal degrees), longitude
(decimal degrees), wavelength (nm), Rrc, and Rrc difference). The Rrc difference is the difference between
the selected location (with oil) and a nearby pixel (to represent the water reflectance and a non-oil pixel).
MISR: Nine Multi-angle Imaging Spectroradiometer (MISR) Top-of-Atmosphere radiance (TOA) data HDF files from
May 17, 2010 in the Mississippi Delta region. The filenames are
MISR_AM1_GRP_TERRAIN_LM_Pmmm_Onnnnnn_cc_SITE_MISSDELTA_Fff_vvvv .hdf, where MISR_AM1_GRP_TERRAIN mean
Level1B2 geo-rectified radiances, LM means the ellipsoid-projected local mode, Pmmm corresponds to the orbit
path number, Onnnnnn is the absolute orbit number, cc is the camera identifier, ff is the file format
version and vvvv is the version number (which relates to the reprocessing of a dataset with different
software and/or ancillary inputs). The parameters defined to carry a Radiometric Data Quality Indicator
(RDQI) associated with each measurement. The files contain geometric parameters which measure the sun and
view angles at the reference ellipsoid; and Bidirectional Reflectance Factor (BRF) conversion factor for
each band. Each file has 4 spectral bands: blue (446 nm), green (558 nm), red (672 nm) and near-infrared
(NIR, 867 nm); and 9 viewing angles: 0, 26.1, 45.6, 60, 70.5, -26.1, -45.6, -60, -70.5 degrees. Each file
has a corresponding XML file with metadata. The radiance data were extracted at selected locations
(MISR_05_17_2010.xlsx) with emulsion, thin oil, thick oil and water for all angles and spectral bands.
Radiance was also extracted at a transect for only the blue and NIR spectral bands and all angles.
MERIS: Three Medium Resolution Imaging Spectrometer (MERIS) Rrc data files for April 25, 2010, April 26, 2010,
and April 28, 2010 in HDF format processed with the software SeaDAS (version 7.0) at a 250 m spatial
resolution and mapped to a cylindrical equidistant projection [bounding box (North, South, West, East;
decimal degrees) = 31, 26, -91, -84]. The HDF files contain the Rrc data at the following wavelengths (nm):
413, 443, 490, 510, 560, 620, 665, 681, 709, 754, 762, 779, 865, and 885; the viewing geometry [solar zenith
(solz), sensor zenith (senz), solar azimuth (sola), and sensor azimuth (sena)]; and glint coefficient
(glint_coef), relative azimuth angle (phi), ozone, wind and pressure. An RGB true-color image was created
for each MERIS file. The Rrc data was extracted at selected locations and saved in excel files (latitude
(decimal degrees), longitude (decimal degrees), site name, wavelength (nm), Rrc, and Rrc difference).
ETM+: One Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Rrc data file in netCDF format processed with the
software ACOLITE (V20170718.0) at a 30 m spatial resolution. The netCDF file contains chlorophyll-a derived
with the OC3 algorithm (CHL-OC3), diffuse attenuation at 490 nm derived with the QAA algorithm (KD490),
quality control flags, Scattering Line Height (SLH), latitude (decimal degrees), longitude (decimal
degrees), the Rrc data at the following wavelengths (nm): 479, 561, 661, 835, 1650, and 2209. An RGB
true-color image was created for the Landsat file. The Rrc data was extracted at selected locations and
saved in excel files (latitude (decimal degrees), longitude (decimal degrees), site name, wavelength (nm),
Rrc, and Rrc difference).
AVIRIS_Classification_Result: The original AVIRIS file can be accessed openly via
https://pubs.usgs.gov/of/2010/1167/downloads/figure16c-geotiff.tif. The reflectance spectra were extracted
at selected locations and saved in AVIRIS_05_17_2010.xlsx (latitude (decimal degrees), longitude (decimal
degrees), site name, wavelength (nm), Reflectance, and Reflectance difference). The
emulsion_relative_thickness and non_emulsion_relative_thickness are the resulted relative thickness of oil
emulsion and non-emulsions following the proposed classification scheme. The AVIRIS_scatter_plot.xlsx file
contains the comparison of the relative thickness of oil emulsions from the multiband classification scheme
with the USGS derived oil emulsion thickness using a hyperspectral algorithm. The
AVIRIS_Classification_Result.xlsx includes data of mean and standard deviation spectra of classified pixels,
using both classification approaches.
MODIS_Classification_Result: This folder includes the results of MODIS relative thickness of oil emulsion
following the same classification scheme and an AVIRIS derived emulsion thickness derived by USGS after
binning to 250-m spatial resolution.
LGN_vs_Rrc_Difference.xlsx: contains id, MODIS_filename, year, month, day, latitude (decimal degrees),
longitude (decimal degrees), oil-water Rrc difference (i.e., Rrc difference between oil pixels and nearby
oil-free pixels in the MODIS 859 nm band), sun glint strength (LGN) calculated from >300 natural slicks in
the Gulf of Mexico using MODIS data. LGN was estimated using Cox and Munk (1954) model. The values represent
the mean of the extracted oil slick (a polygon).|AVIRIS and MODIS Classification: The classification scheme
is a step-wise model to classify oil type (emulsion status) and classify relative thickness of both oil
emulsions and non-emulsions using multiband satellite data; The USGS method using a hyperspectral approach
to quantitatively map oil emulsions using hyperspectral AVIRIS data. Please refer to Sun and Hu, 2018 for
further details of the classification scheme and its comparison with the USGS approach.||||Cox, C., & Munk,
W. (1954). Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter. Journal of
the Optical Society of America, 44(11), 838. doi:10.1364/josa.44.000838
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Distribution info:
Distribution:
Distributor:
Distributor:
Distributor contact:
Responsible party:
Organisation name:
Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC)
Contact info:
Contact:
Phone:
Telephone:
Voice:
3618253604
Address:
Address:
Delivery point:
6300 Ocean Drive
City:
Corpus Christi
Administrative area:
TX
Postal code:
78412
Country:
USA
Electronic mail address:
griidc@gomri.org
Online Resource:
Online Resource:
Linkage:
URL:
https://data.gulfresearchinitiative.org
Role:
Role code:
distributor
Distributor format:
Format:
Name:
xlsx, hdr, nc, png, hdf, xml, txt
Version:
inapplicable
File decompression technique:
zip
Distributor transfer options:
Digital transfer options:
Transfer size:
1994.3009
Online:
Online Resource:
Linkage:
URL:
https://data.gulfresearchinitiative.org/data/R4.x267.000:0113
Protocol:
https
Name:
Data Landing Page
Description:
GRIIDC dataset landing page
Function:
Online function code:
information
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Metadata maintenance:
Maintenance information:
Maintenance and update frequency:
unknown
Maintenance note:
This ISO metadata record was automatically generated from information provided to GRIIDC for dataset:
R4.x267.000:0113 on 2021-11-29T14:11:39-06:00
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