A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data

Yackel, John J.; Nandan, Vishnu; Mahmud, Mallik; Scharien, Randall K.; Kang, Jason; Geldsetzer, Torsten

doi:10.1016/j.rse.2017.09.030

Cited by 10 publications

(8 citation statements)

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“…However, we assume that a pixel consists of three surface features (open water, melt pond and sea ice), which may cause uncertainties since the Arctic sea ice is actually covered by various surface types rather than only three types (Rösel and Kaleschke, 2012; Rösel and others, 2012). For example, there are at least five types of melt ponds during the melting period (Yackel and others, 2017). Furthermore, it is necessary to consider more classes like melt ponds with different types, wet snow, bare ice or sediment-laden surfaces (Rösel and others, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…The generated MPF products achieved an 8-day temporal resolution during the melting period in the Arctic from 2000 to 2011 based on MODIS reflectance data. Considering the different reflection characteristics of different types of melt ponds, Yackel and others (2017) improved this method by distinguishing different types of ponds to improve the accuracy of MPF retrieval. The mechanisms of melt pond formation and evolution were studied using field data, showing a great difference in melt ponds on first-year and multiyear ice (Eicken and others, 2002; Polashenski and others, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Qing Li and others five types of melt ponds during the melting period (Yackel and others, 2017). Furthermore, it is necessary to consider more classes like melt ponds with different types, wet snow, bare ice or sediment-laden surfaces (Rösel and others, 2012).…”

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confidence: 99%

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Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

Zhou

Zheng

et al. 2020

Ann. Glaciol.

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Abstract The evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

Zhou

Zheng

et al. 2020

Ann. Glaciol.

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“…In order to simulate the reflectance spectra of images from airborne or spaceborne sensors, we resampled the measured spectra to the spectral resolution of some commonly used sensors [ 29 ]. First, the wavelengths and full width at half maximum (FWHM) information of five commonly used sensors, namely GF-2, Landsat8-OLI, Sentinel3-OLCI, MODIS, and AVIRIS, were obtained.…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction

Liu

et al. 2018

Sensors

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Researchers have studied oil spills in open waters using remote sensors, but few have focused on extracting reflectance features of oil pollution on sea ice. An experiment was conducted on natural sea ice in Bohai Bay, China, to obtain the spectral reflectance of oil-contaminated sea ice. The spectral absorption index (SAI), spectral peak height (SPH), and wavelet detail coefficient (DWT d5) were calculated using stepwise multiple linear regression. The reflectances of some false targets were measured and analysed. The simulated false targets were sediment, iron ore fines, coal dust, and the melt pool. The measured reflectances were resampled using five common sensors (GF-2, Landsat8-OLI, Sentinel3-OLCI, MODIS, and AVIRIS). Some significant spectral features could discriminate between oil-polluted and clean sea ice. The indices correlated well with the oil area fractions. All of the adjusted R2 values exceeded 0.9. The SPH model1, based on spectral features at 507–670 and 1627–1746 nm, displayed the best fitting. The resampled data indicated that these multi-spectral and hyper-spectral sensors could be used to detect crude oil on the sea ice if the effect of noise and spatial resolution are neglected. The spectral features and their identified changes may provide reference on sensor design and band selection.

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“…To simulate the spectrum from airborne or spaceborne sensor images, the spectral resolution of ASTER and AVHRR was resampled [33,34]. The wavelength and full width at half maximum (FWHM) information of ASTER and AVHRR were obtained, and the spectral resampling module in ENVI (v. 5.5, Harris Corporation) used to resample the spectrum.…”

Section: Thermal Infrared Sensor Data Simulationmentioning

confidence: 99%

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Guo

Liu

2020

JMSE

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Remote sensing is an important method for monitoring marine oil-spill accidents. However, methods for measuring oil-film thickness remain insufficient. Due to the stable differences in the surface emissivity and temperature of oil and water, the oil film can be detected using thermal infrared. This study measured emissivity of seven different oil-film thicknesses and seven different American Petroleum Institute (API) densities, and analyzed the spectral characteristics. Results show an optimal wavelength position for oil-film thickness and fuel API density monitoring is 12.55 µm. Principal component analysis and continuum removal methods were used for data processing. Stepwise multiple linear regression was used to establish relationships between emissivity and oil slick thicknesses and API densities. Oil-film thickness and fuel API density data were analyzed by principal component analysis and continuum removal before regression analysis. The spectral emissivity data was convolved into Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) thermal bands to determine potential of the sensor in oil-film detection. The result shows that neither could be used to estimate thickness. The AVHRR-4 band and band 12 and 13 of the ASTER could be used to separate oils from water and have potential to distinguish different oil types. deficiencies in measuring fuel thickness [12]. Thermal infrared analysis distinguishes oil on the sea surface by thermal comparison. Because the emissivity of water and oil are different, thermal contrast is generated, and the emissivity depends on the thickness of the oil slick [15]. During the daytime, thick oil slicks become warmer than the surrounding water while thinner, detectable slicks appear cooler. This reverses at nighttime [16,17]. A thin film interference theory-based model is used to describe the thickness-dependent contrast between the background water and the sea surface covered by crude oil [18,19]. The thermal infrared emissivity of fuels was measured and analyzed using statistical methods [20][21][22]. At the same time, the different emission spectra of the fuels were detected, and a relationship between the fuel emissivity and the American Petroleum Institute (API) density was obtained.At present, thermal infrared technology is widely used in vegetation and soil detection. Among these [23], the relationship between the emissivity spectrum of the thermal infrared region and the leaf area index is obtained. The thermal infrared region is used to study soil moisture [24], and it has been proven that the long-wave infrared band is more accurate than visible-near infrared and shortwave infrared for the prediction of soil properties [25]. Some studies have applied thermal infrared technology to fuel-oil measurements, using regression analyses, to test the degree of soil oil pollution [26], and through experiments have identified midday as the optimal detection time for oil slicks [27]. The relationship between the...

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A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data

Cited by 10 publications

References 60 publications

Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

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