scholarly journals Spectral Calibration Requirements of Radio Interferometers for Epoch of Reionisation Science with the SKA

2016 ◽  
Vol 33 ◽  
Author(s):  
Cathryn M. Trott ◽  
Randall B. Wayth
Keyword(s):  
Thermal Noise ◽  
Spectral Response ◽  
Order Term ◽  
Spectral Characteristics ◽  
Primary Beam ◽  
Noise Power ◽  
Science Data ◽  
Spectral Calibration ◽  
Fourth Order Term ◽  
Complete Frequency

AbstractSpectral features introduced by instrumental chromaticity of radio interferometers have the potential to negatively impact the ability to perform Epoch of Reionisation and Cosmic Dawn (EoR/CD) science. We describe instrument calibration choices that influence the spectral characteristics of the science data, and assess their impact on EoR/CD statistical and tomographic experiments. Principally, we consider the intrinsic spectral response of the antennas, embedded within a complete frequency-dependent primary beam response, and instrument sampling. The analysis is applied to the proposed SKA1-Low EoR/CD experiments. We provide tolerances on the smoothness of the SKA station primary beam bandpass, to meet the scientific goals of statistical and tomographic (imaging) of EoR/CD programs. Two calibration strategies are tested: (1) fitting of each fine channel independently, and (2) fitting of annth-order polynomial for each ~ 1 MHz coarse channel with (n+1)th-order residuals (n= 2, 3, 4). Strategy (1) leads to uncorrelated power in the 2D power spectrum proportional to the thermal noise power, thereby reducing the overall sensitivity. Strategy (2) leads to correlated residuals from the fitting, and residual signal power with (n+1)th-order curvature. For the residual power to be less than the thermal noise, the fractional amplitude of a fourth-order term in the bandpass across a single coarse channel must be < 2.5% (50 MHz), < 0.5% (150 MHz), < 0.8% (200 MHz). The tomographic experiment places constraints on phase residuals in the bandpass. We find that the root-mean-square variability over all stations of the change in phase across any fine channel (4.578 kHz) should not exceed 0.2 degrees.

scholarly journals Preflight Spectral Calibration of Airborne Shortwave Infrared Hyperspectral Imager with Water Vapor Absorption Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2259 ◽  
Author(s):  
Honglin Liu ◽  
Dong Zhang ◽  
Yueming Wang
Keyword(s):  
Water Vapor ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Calibration Method ◽  
Least Square ◽  
Response Functions ◽  
Water Vapor Absorption ◽  
Gaussian Shape ◽  
Spectral Calibration ◽  
Calibration Uncertainty

Due to the strong absorption of water vapor at wavelengths of 1350–1420 nm and 1820–1940 nm, under normal atmospheric conditions, the actual digital number (DN) response curve of a hyperspectral imager deviates from the Gaussian shape, which leads to a decrease in the calibration accuracy of an instrument’s spectral response functions (SRF). The higher the calibration uncertainty of SRF, the worse the retrieval accuracy of the spectral characteristics of the targets. In this paper, an improved spectral calibration method based on a monochromator and the spectral absorptive characteristics of water vapor in the laboratory is presented. The water vapor spectral calibration method (WVSCM) uses the difference function to calculate the intrinsic DN response functions of the spectral channels located in the absorptive wavelength range of water vapor and corrects the wavelength offset of the monochromator via the least-square procedure to achieve spectral calibration throughout the full spectral responsive range of the hyper-spectrometer. The absolute spectral calibration uncertainty is ±0.125 nm. We validated the effectiveness of the WVSCM with two tunable semiconductor lasers, and the spectral wavelength positions calibrated by lasers and the WVSCM showed a good degree of consistency.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

Effects of Metal Stress on Visible/Near-Infrared Reflectance Spectra of Vegetation

2011 ◽  
Vol 347-353 ◽  
pp. 2735-2738 ◽  
Author(s):  
Guang Yu Chi ◽  
Yi Shi ◽  
Xin Chen ◽  
Jian Ma ◽  
Tai Hui Zheng
Keyword(s):  
Heavy Metal ◽  
Structural Changes ◽  
Near Infrared ◽  
Leaf Surface ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Surface Characteristics ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Near Infrared Reflectance

Vegetation which suffers from heavy metal stresses can cause changes of leaf color, shape and structural changes. The spectral characteristics of vegetation leaves is related to leaf thickness, leaf surface characteristics, the content of water, chlorophyll and other pigments. So the eco-physiology changes of plants can be reflected by spectral reflectance. Studies on the spectral response of vegetation to heavy metal stress can provide a theoretical basis for remote sensing monitoring of metal pollution in soils. In recent decades, there are substantial amounts of literature exploring the effects of heavy metals on vegetation spectra.

QUANTUM TUNNELLING OR THERMAL HOPPING

2000 ◽  
Vol 14 (19n20) ◽  
pp. 2109-2116
Author(s):  
N. PANCHAPAKESAN
Keyword(s):  
Quantum Tunneling ◽  
Condensed Matter ◽  
Order Term ◽  
Thick Wall ◽  
Thin Wall ◽  
Quantum Tunnelling ◽  
First Order ◽  
Classical State ◽  
Fourth Order Term ◽  
Loss Of Coherence

The nature of the transition from the quantum tunneling regime to the thermal hopping regime has importance in the study of condensed matter physics and cosmological phase transitions. It may also be of significance in collapse from quantum state to a classical state due to measurement (or loss of coherence due to some other process). We study this transition analytically in scalar field theory with a fourth order term. We obtain analytic bounce solutions which correctly give the action in thin and thick wall limits of the potential. We find that the transition is of the second order for the case of thick wall while it seems to be of first order for the case of thin wall.

Effects of Bias Spectral Characteristics on Spectral Response of Solar Cells

2017 ◽  
Vol 54 (10) ◽  
pp. 100401
Author(s):  
时 强 Shi Qiang ◽  
卞洁玉 Bian Jieyu ◽  
刘正新 Liu Zhengxin
Keyword(s):  
Solar Cells ◽  
Spectral Response ◽  
Spectral Characteristics

Higher‐order moveout spectra

Geophysics ◽  
1979 ◽  
Vol 44 (7) ◽  
pp. 1193-1207 ◽  
Author(s):  
Bruce T. May ◽  
Donald K. Straley
Keyword(s):  
Fourth Order ◽  
Seismic Reflection ◽  
Order Term ◽  
Higher Order ◽  
Second Order ◽  
Equation Modeling ◽  
Fourth Order Term ◽  
Higher Order Terms ◽  
Velocity Spectra

Higher‐order terms in the generalized seismic reflection moveout equation are usually neglected, resulting in the familiar second‐order, or hyperbolic, moveout equation. Modeling studies show that the higher‐order terms are often significant, and their neglect produces sizable traveltime residuals after correction for moveout in such cases as kinked‐ray models. Taner and Koehler (1969) introduced velocity spectra for estimating stacking velocity defined on the basis of second‐order moveout. Through the use of orthogonal polynomials, an iterative procedure is defined that permits computation of fourth‐order moveout spectra while simultaneously upgrading the previously computed, second‐order spectra. Emphasis is placed on the fourth‐order term, but the procedure is general and can be expanded to higher orders. When used with synthetic and field recorded common‐midpoint (CMP) trace data, this technique produces significant improvements in moveout determination affecting three areas: (1) resolution and interpretability of moveout spectra, (2) quality of CMP stacked sections, and (3) computation of velocity and depth for inverse modeling.

scholarly journals Spectral Calibration Algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS)

2020 ◽  
Vol 12 (17) ◽  
pp. 2846
Author(s):  
Mina Kang ◽  
Myoung-Hwan Ahn ◽  
Xiong Liu ◽  
Ukkyo Jeong ◽  
Jhoon Kim
Keyword(s):  
Solar Irradiance ◽  
Spectral Response ◽  
Nonlinear Least Squares ◽  
Reference Spectrum ◽  
Test Results ◽  
Environment Monitoring ◽  
Spectral Calibration ◽  
Sensitivity Tests ◽  
Calibration Algorithm ◽  
Least Squares Approach

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korean Multi-Purpose Satellite 2B was successfully launched in February 2020. GEMS is a hyperspectral spectrometer measuring solar irradiance and Earth radiance in the wavelength range of 300 to 500 nm. This paper introduces the spectral calibration algorithm for GEMS, which uses a nonlinear least-squares approach. Sensitivity tests for a series of unknown algorithm parameters such as spectral range for fitting, spectral response function (SRF), and reference spectrum were conducted using the synthetic GEMS spectrum prepared with the ground-measured GEMS SRF. The test results show that the required accuracy of 0.002 nm is achievable provided the SRF and the high-resolution reference spectrum are properly prepared. Such a satisfactory performance is possible mainly due to the inclusion of additional fitting parameters of spectral scales (shift, squeeze, and high order shifts) and SRF (width, shape and asymmetry). For the application to the actual GEMS data, in-orbit SRF is to be monitored using an analytic SRF function and the measured GEMS solar irradiance, while a reference spectrum is going to be selected during the instrument in-orbit test. The calibrated GEMS data is expected to be released by the end of 2020.

SURFACE CHARACTERISTICS OF MAPPING UNITS RELATED TO AERIAL IMAGING OF SOILS

1973 ◽  
Vol 53 (3) ◽  
pp. 249-257 ◽  
Author(s):  
J. CIHLAR ◽  
R. PROTZ
Keyword(s):  
Organic Matter ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Soil Surface ◽  
Surface Characteristics ◽  
Aerial Images ◽  
Recording Device ◽  
Color Coordinates ◽  
Additional Information ◽  
Mapping Units

Successful detection of boundaries of soil mapping units (SMU's) using differences in spectral characteristics of surface soils requires that any given SMU appears homogeneous but different from all other SMU's as viewed by the recording device. The purpose of this study was to assess surface characteristics affecting aerial images of soils and the degree to which they differ among SMU's. Soil surface samples were collected along four transects (crossing 11 SMU's) at 10-m intervals. Munsell color coordinates (dry soils) and organic matter, oxalate iron, sand, silt and clay contents were subjected to a statistical analysis. The results showed that except for Munsell hue, more than 50% of the total variability occurred among SMU's. Distinct groups of SMU's were denned on the basis of Munsell value, organic matter, sand, and silt content, but in no case were all SMU s distinguished from one another. This and related studies suggest that these soils could not be mapped accurately on the basis of their spectral response alone but spectral differences would provide additional information on soil distribution Since soil reflectance characteristics are described in terms of Munsell color coordinates and other properties measured by pedologists, these results may be extrapolated into various areas with similar soils.

A Unified Explanation of gm/ID-Based Noise Analysis

2014 ◽  
Vol 24 (01) ◽  
pp. 1550010 ◽  
Author(s):  
Jack Ou ◽  
Pietro M. Ferreira
Keyword(s):  
Thermal Noise ◽  
Flicker Noise ◽  
Practical Implication ◽  
Noise Analysis ◽  
Drain Current ◽  
Corner Frequency ◽  
Noise Power ◽  
Noise Power Spectral Density ◽  
Power Spectral ◽  
The Relationship

We present an unified explanation of the transconductance-to-drain current (gm/ID)-based noise analysis in this paper. We show that both thermal noise coefficient (γ) and device noise corner frequency (f co ) are dependent on the gm/ID of a transistor. We derive expressions to demonstrate the relationship between the normalized noise power spectral density technique and the technique based on γ and f co . We conclude this letter with examples to demonstrate the practical implication of our study. Our results show that while both techniques discussed in this letter can be used to compute noise numerically, using γ and f co to separate thermal noise from flicker noise provides additional insight for optimizing noise.

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