Exploring for natural gas using reflectance spectra of surface soils

Xu, Dan; Ni, Guoqiang; Jiang, Lili; Shen, Yuanting; Li, Ting; Ge, Shule; Shu, Xian-Biao

doi:10.1016/j.asr.2007.05.073

Cited by 35 publications

(20 citation statements)

References 8 publications

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“…The Hyperion sensor, on board the orbiting EO-1 (Earth Observing One) satellite, collects data with a 30 m pixel size in 242 bands in the visible, near-infrared, and shortwave infrared ranges [60,61]. Though Hyperion images are characterized by relatively high levels of noise [59,[62][63][64][65][66][67][68], they represent important alternatives to datasets generated by orbiting multispectral systems. In numerous case studies, Hyperion images have proven useful in the mapping of lithological and mineralogical classes [63,64,67,[69][70][71][72][73][74][75][76].…”

Section: Hyperion Imagesmentioning

confidence: 99%

Detection and Mapping of Black Rock Coatings Using Hyperion Images: Sudbury, Ontario, Canada

Leverington

Schindler

2016

Remote Sensing

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Base metal smelting activities can produce acidic rain that promotes vegetation loss and the development of black coatings on bedrock. Such coatings can form over large areas and are among the most prominent long-term vestiges of past smelting activities. In this study, multispectral images derived from Hyperion reflectance data were evaluated with regard to their utility in the discrimination and mapping of black rock coatings near Sudbury. Spectral angle mapper (SAM) classifications generated on the basis of image-derived endmember spectra could not be used to properly identify major exposures of coated bedrock without also producing substantial confusion with uncoated classes. Neural network and maximum likelihood classifications produced improved representations of the spatial distribution of coated bedrock, though confusion between coated and uncoated classes is problematic in most outputs. Maximum likelihood results generated using a null class are noteworthy for their effectiveness in highlighting exposures of coated bedrock without substantial confusion with uncoated classes. Although challenges remain, classification results confirm the potential of remote sensing techniques for use in the worldwide detection, mapping, and monitoring of coating-related environmental degradation in the vicinities of base metal smelters.

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Section: Hyperion Imagesmentioning

confidence: 99%

Detection and Mapping of Black Rock Coatings Using Hyperion Images: Sudbury, Ontario, Canada

Leverington

Schindler

2016

Remote Sensing

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“…Therefore, the changes in multipath during the time (velocity of multipath signature (V δΦ )) related to the changes in the satellite elevation angle (ε) (velocity of satellite elevation angle (V ε )) can be defined as: (4) and simplifying Equation (4) gives:…”

Section: Multipath Variations With Respect To Satellite Elevation Angmentioning

confidence: 99%

“…GPS reflectometry provides a way to infer the surface properties remotely because the reflectance spectra at radio wavelengths contain properties about the surface compositions [3][4][5]. The high reflectivity of GPS signals in the frequency range of L-Band (1.2276 and 1.5754 GHz) on the liquid water and the frozen water (e.g., ice caps, glaciers, etc.)…”

Section: Introductionmentioning

confidence: 99%

Physical Reflectivity and Polarization Characteristics for Snow and Ice-Covered Surfaces Interacting with GPS Signals

Najibi

Jin

2013

Remote Sensing

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Abstract:The Global Positioning System (GPS) reflected signal has been demonstrated to remotely sense the oceans, land surfaces and the cryosphere, including measuring snow depth, soil moisture, vegetation growth and wind direction. Since the Earth surface's characteristics are very complex, the surface reflectivity process and interaction with GPS signals is not well understood. In this study, we investigate the surface's reflectivity and variability of snow and ice surfaces interacting with GPS L1 and L2 signals in order to retrieve multipath signals and infer surface characteristics by using the direct and reflected polarizations of each signal. Firstly, the effects of both GPS satellite elevation angle and GPS receiver's antenna height variations on the multipath signal variability have been investigated by numerical formulations. Secondly, the specular reflection coefficients' features and the total surface polarization for liquid and solid surfaces are discussed. Moreover, the linear polarization and circular polarizations (co-polarized and cross-polarized) as well as their corresponding convolution functions are developed horizontally and vertically. The results show that the multipath signals are more sensitive to the satellite elevation angle variations than to changes in the GPS receiver's antenna height. The convolution function demonstrates that the snowy surface has a minimum reflectance in circular polarization but maximum reflectance in linear polarization. GPS signals reflecting from an ice-covered surface show a maximum value in circular polarization reflectance and a minimum for linear polarization reflectance. Moreover, the values for reflection from soils are between those for snow and ice in all polarization types. The placement of soil surface reflectance values between snowy and icy surface ones may be noteworthy in new remote sensing applications.

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“…Hyperspectral sensors are able to register reflected light from land surface elements in many narrow continuous spectral bands from the visible to the shortwave infrared parts of the electromagnetic spectrum. 7 this allows hyperspectral systems to provide spectral information useful for many applications but not limited to land cover maps. 8 The launch of the Hyperion space-borne hyperspectral sensor in 2000 under NASA's New Millennium Program on-board the Earth Observer-1 (EO-1) satellite platform.…”

Section: Introductionmentioning

confidence: 99%

Land-Cover Classification for East Suez Canal Region Using Hyperspectral Eo-1 Data

Afify¹,

Sheta²,

Arafat³

et al. 2017

ECB

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Earth Observation (EO-1) data provides a highest spectral resolution to get spectral information of Earth's Surface targets within 242 spectral bands at 30 m spatial resolution. In this context, the main objective of this paper is to produce a land cover map using hyperspectral data acquired by EO-1 Hyperion instrument over one test site. Atmospheric correction on the hyperspectral data was performed using ENVI's Fast Line-of-sight Atmospheric Analysis of Spectral Hyper-cubes (FLAASH) module. Support Vector Machine (SVM) classification was implemented on the dominant elements to produce a land cover map for test site. SVM is carried out in this research to deal with the multi-class issue of Hyperion data. Classification using the kernel functions in classification made the classifier robust against the outliers. The Land Cover Classification System (LCCS) was used to know the land cover classes. The result showed high accuracy for land cover map with machine learning classifier like SVM using hyperspectral remote sensing data. The overall classification accuracy obtained was 97.85.

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Exploring for natural gas using reflectance spectra of surface soils

Cited by 35 publications

References 8 publications

Detection and Mapping of Black Rock Coatings Using Hyperion Images: Sudbury, Ontario, Canada

Detection and Mapping of Black Rock Coatings Using Hyperion Images: Sudbury, Ontario, Canada

Physical Reflectivity and Polarization Characteristics for Snow and Ice-Covered Surfaces Interacting with GPS Signals

Land-Cover Classification for East Suez Canal Region Using Hyperspectral Eo-1 Data

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