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 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 Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2195
Author(s):  
Lucas de Paula Corrêdo ◽  
Leonardo Felipe Maldaner ◽  
Helizani Couto Bazame ◽  
José Paulo Molin
Keyword(s):  
Sample Preparation ◽  
Prediction Models ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Partial Least Square ◽  
Least Square ◽  
Sample Type ◽  
Sampling Strategies ◽  
Significant Difference

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

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.

scholarly journals A Feasibility Study for Simultaneous Estimates of Water Vapor and Precipitation Parameters Using a Three-Frequency Radar

2005 ◽  
Vol 44 (10) ◽  
pp. 1511-1525 ◽  
Author(s):  
R. Meneghini ◽  
L. Liao ◽  
L. Tian
Keyword(s):  
Water Vapor ◽  
Rayleigh Scattering ◽  
Pulse Compression ◽  
Pulse Repetition Frequency ◽  
Center Frequency ◽  
Water Vapor Absorption ◽  
Single Antenna ◽  
Vapor Absorption ◽  
The Cost ◽  
Lower Frequencies

Abstract The radar return powers from a three-frequency radar, with center frequency at 22.235 GHz and upper and lower frequencies chosen with equal water vapor absorption coefficients, can be used to estimate water vapor density and parameters of the precipitation. A linear combination of differential measurements between the center and lower frequencies on one hand and the upper and lower frequencies on the other provide an estimate of differential water vapor absorption. The coupling between the precipitation and water vapor estimates is generally weak but increases with bandwidth and the amount of non-Rayleigh scattering of the hydrometeors. The coupling leads to biases in the estimates of water vapor absorption that depend primarily on the phase state and the median mass diameter of the hydrometeors. For a down-looking radar, path-averaged estimates of water vapor absorption are possible under rain-free as well as raining conditions by using the surface returns at the three frequencies. Simulations of the water vapor attenuation retrieval show that the largest source of error typically arises from the variance in the measured radar return powers. Although the error can be mitigated by a combination of a high pulse repetition frequency, pulse compression, and averaging in range and time, the radar receiver must be stable over the averaging period. For fractional bandwidths of 20% or less, the potential exists for simultaneous measurements at the three frequencies with a single antenna and transceiver, thereby significantly reducing the cost and mass of the system.

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