Relationship between bulk scattering, sensory texture and water spectral pattern in ‘Braeburn’ apples

Landsat data used for monitoring activities to land cover because it has spatial resolution and high temporal. To monitor land cover changes in an area, atmospheric correction is needed to be performed in order to obtain data with precise digital value picturing current condition. This study compared atmospheric correction methods namely Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The correction results then were compared to Surface Reflectance (SR) imagery data obtained from the United States Geological Survey (USGS) satelite. The three atmospheric correction methods were applied to Landsat OLI data path/row126/62 for 3 particular dates. Then, sample on vegetation, soil and bodies of water (waterbody) were retrieved from the image. Atmospheric correction results were visually observed and compared with SR sample on the absolute value, object spectral patterns, as well as location and time consistency. Visual observation indicates that there was a contrast change on images that had been corrected by using FLAASH method compared to SR, which mean that the atmospheric correction method was quite effective. Analysis on the object spectral pattern, soil, vegetation and waterbody of images corrected by using FLAASH method showed that it was not good enough eventhough the reflectant value differed greatly to SR image. This might be caused by certain variables of aerosol and atmospheric models used in Indonesia. QUAC and DOS made more appropriate spectral pattern of vegetation and water body than spectral library. In terms of average value and deviation difference, spectral patterns of soil corrected by using DOS was more compatible than QUAC.

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Characterization of the sources and processes of organic and inorganic aerosols in New York City with a high-resolution time-of-flight aerosol mass spectrometer

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-10-22669-2010 ◽

2010 ◽

Vol 10 (10) ◽

pp. 22669-22723 ◽

Cited By ~ 5

Author(s):

Y.-L. Sun ◽

Q. Zhang ◽

J. J. Schwab ◽

K. L. Demerjian ◽

W.-N. Chen ◽

...

Keyword(s):

New York ◽

New York City ◽

High Resolution ◽

Mass Spectrometer ◽

York City ◽

Time Of Flight ◽

Resolution Time ◽

Aerosol Mass Spectrometer ◽

Spectral Pattern ◽

Aerosol Mass

Abstract. Submicron aerosol particles (PM1) were measured in-situ using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) during the summer 2009 Field Intensive Study at Queens College in New York City. Organic aerosol (OA) and sulfate are the two dominant species, accounting for 54% and 24%, respectively, of total PM1 mass on average. The average mass size distribution of OA presents a small mode peaking at ~150 nm (Dva) in addition to an accumulation mode (~550 nm) that is internally mixed with sulfate, nitrate, and ammonium. The diurnal cycles of sulfate and OA both show pronounced peaks between 01:00–02:00 p.m. EST due to photochemical production. The average (±1σ) oxygen-to-carbon (O/C), hydrogen-to-carbon (H/C), and nitrogen-to-carbon (N/C) ratios of OA in NYC are 0.36 (±0.09), 1.49 (±0.08), and 0.012(±0.005), respectively, corresponding to an average organic mass-to-carbon (OM/OC) ratio of 1.62(±0.11). Positive matrix factorization (PMF) of the high resolution mass spectra identified five OA components: a hydrocarbon-like OA (HOA), two types of oxygenated OA (OOA) including a low-volatility OOA (LV-OOA) and a semi-volatile OOA (SV-OOA), a cooking-emission related OA (COA), and a unique nitrogen-enriched OA (NOA). HOA appears to represent primary OA (POA) from urban traffic emissions. It comprises primarily of reduced species (H/C=1.83; O/C=0.06) and shows a mass spectral pattern very similar to those of POA from fossil fuel combustion, and correlates tightly with traffic emission tracers including elemental carbon and NOx. LV-OOA, which is highly oxidized (O/C=0.63) and correlates well with sulfate, appears to be representative for regional, aged secondary OA (SOA). SV-OOA, which is less oxidized (O/C=0.38) and correlates well with non-refractory chloride, likely represents less photo-chemically aged, semi-volatile SOA. COA shows a similar spectral pattern to the reference spectra of POA from cooking emissions and a distinct diurnal pattern peaking around local lunch and dinner times. In addition, NOA is characterized with prominent CxH2x+2N+ peaks likely from amine compounds. Our results indicate that cooking-related activities are a major source of POA in NYC, releasing comparable amounts of POA as traffic emissions. POA=HOA+COA) on average accounts for ~30% of the total OA mass during this study while SOA dominates the OA composition with SV-OOA and LV-OOA on average accounting for 34% and 30%, respectively, of the total OA mass. The chemical evolution of SOA in NYC involves a~continuous oxidation from SV-OOA to LV-OOA, which is further supported by a gradual increase of O/C ratio and a simultaneous decrease of H/C ratio in total OOA. Detailed analysis of NOA (5.8% of OA) presents evidence that nitrogen-containing organic species such as amines might have played an important role in the atmospheric processing of OA in NYC, likely involving acid-base chemistry. Analysis of air mass trajectories and satellite imagery of aerosol optical depth (AOD) indicates that the high potential source regions of secondary sulfate and aged OA are mainly located in regions to the west and southwest of the city.

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Spectral pattern, harmonic relations, and the perceptual grouping of low-numbered components

The Journal of the Acoustical Society of America ◽

10.1121/1.1605411 ◽

2003 ◽

Vol 114 (4) ◽

pp. 2118-2134 ◽

Cited By ~ 9

Author(s):

Brian Roberts ◽

Jeffrey M. Brunstrom

Keyword(s):

Perceptual Grouping ◽

Spectral Pattern

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Spectral Pattern Recognition

Optical Engineering ◽

10.1117/12.7973612 ◽

1985 ◽

Vol 24 (6) ◽

Cited By ~ 1

Author(s):

Donald S. Frankel ◽

O. I. Sindoni

Keyword(s):

Pattern Recognition ◽

Spectral Pattern

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Angle resolved scatter measurement of bulk scattering in transparent ceramics

10.1117/12.2175771 ◽

2015 ◽

Author(s):

Saurabh Sharma ◽

J. Keith Miller ◽

Ramesh K. Shori ◽

Mark S. Goorsky

Keyword(s):

Transparent Ceramics ◽

Bulk Scattering

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Identification of Geospatial Objects Using Spectral Pattern

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch038 ◽

2019 ◽

pp. 882-893

Author(s):

Subhabrata Barman

Keyword(s):

Spectral Reflectance ◽

Target Surface ◽

Classification Rules ◽

Spectral Image ◽

Incident Wavelength ◽

Reflectance Curve ◽

Spectral Pattern ◽

Multispectral Remote Sensing ◽

Unique Signature ◽

Absorption Characteristics

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.

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