Photometric Correction of Images of Visible and Near-Infrared Bands from Chandrayaan-1 Hyper-Spectral Imager (HySI)

2021 ◽  
Vol 126 (1) ◽  
Author(s):  
Subhadyouti Bose ◽  
Mili Ghosh Nee Lala ◽  
Akhouri Pramod Krishna
Keyword(s):  
Near Infrared ◽  
Hyper Spectral ◽  
Spectral Imager

scholarly journals Hyper-Spectral Imager in visible and near-infrared band for lunar compositional mapping

2005 ◽  
Vol 114 (6) ◽  
pp. 721-724 ◽  
Author(s):  
A. S. Kiran Kumar ◽  
A. Roy Chowdhury
Keyword(s):  
Near Infrared ◽  
Infrared Band ◽  
Hyper Spectral ◽  
Spectral Imager

scholarly journals Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

2016 ◽  
Vol 22 (2) ◽  
pp. 267-277
Author(s):  
Baicheng Li ◽  
Baolu Hou ◽  
Yao Zhou ◽  
Mantong Zhao ◽  
Dawei Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Imaging ◽  
Hyper Spectral ◽  
Hyper Spectral Imaging

The long-term vicarious and cross calibration plan for Hyper-spectral Imager Suite (HISUI)

2011 ◽  
Author(s):  
Akihide Kamei ◽  
Kazuki Nakamura ◽  
Tetsushi Tachikawa ◽  
Hirokazu Yamamoto ◽  
Ryosuke Nakamura ◽  
...  
Keyword(s):  
Hyper Spectral ◽  
Cross Calibration ◽  
Spectral Imager

Optical System of the Hyper-spectral Imager for the Underwater Environment and Targets Monitoring

2018 ◽  
Vol 47 (11) ◽  
pp. 1101003 ◽  
Author(s):  
于磊 YU Lei ◽  
徐明明 XU Ming-ming ◽  
陈结祥 CHEN Jie-xiang ◽  
薛辉 XUE Hui
Keyword(s):  
Optical System ◽  
Underwater Environment ◽  
Hyper Spectral ◽  
Spectral Imager

scholarly journals Comparison of the Lunar Models Using the Hyper-Spectral Imager Observations in Lijiang, China

2020 ◽  
Vol 12 (11) ◽  
pp. 1878
Author(s):  
Yang Wang ◽  
Xiuqing Hu ◽  
Lin Chen ◽  
Yu Huang ◽  
Zhanfeng Li ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Ratio ◽  
Spectral Irradiance ◽  
Lunar Phase ◽  
Absorption Bands ◽  
Absorption Region ◽  
Phase Dependence ◽  
Imaging Spectrometer ◽  
Lunar Phases ◽  
Hyper Spectral

A lunar observation campaign was conducted using a hyper-spectral imaging spectrometer in Lijiang, China from December 2015 to February 2016. The lunar hyper-spectral images in the visible to near-infrared region (VNIR) have been obtained in different lunar phases with absolute scale established by the National Institute of Metrology (NIM), China using the lamp–plate calibration system. At the same time, the aerosol optical depth (AOD) is measured regularly by a lidar and a lunar CE318U for atmospheric characterization to provide nightly atmosphere extinction correction of lunar observations. This paper addressed the complicated data processing procedure in detail from raw images of the spectrometer into the spectral lunar irradiance in different lunar phases. The result of measurement shows that the imaging spectrometer can provide lunar irradiance with uncertainties less than 3.30% except for absorption bands. Except for strong atmosphere absorption region, the mean spectral irradiance difference between the measured irradiance and the ROLO (Robotic Lunar Observatory) model is 8.6 ± 2% over the course of the lunar observation mission. The ROLO model performs more reliable to clarify absolute and relative accuracy of lunar irradiance than that of the MT2009 model in different Sun–Moon–Earth geometry. The spectral ratio analysis of lunar irradiance shows that band-to-band variability in the ROLO model is consistent within 2%, and the consistency of the models in the lunar phase and spectrum is well analyzed and evaluated from phase dependence and phase reddening analysis respectively.

scholarly journals An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3469
Author(s):  
Bilawal Abbasi ◽  
Zhihao Qin ◽  
Wenhui Du ◽  
Jinlong Fan ◽  
Chunliang Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Near Infrared ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Optical Remote Sensing ◽  
Statistical Regression ◽  
Sensing Applications ◽  
Medium Resolution ◽  
Spectral Imager

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

The instrument development status of hyper-spectral imager suite (HISUI)

2012 ◽  
Author(s):  
Yoshiyuki Itoh ◽  
Takahiro Kawashima ◽  
Hitomi Inada ◽  
Jun Tanii ◽  
Akira Iwasaki
Keyword(s):  
Instrument Development ◽  
Development Status ◽  
Hyper Spectral ◽  
Spectral Imager

Limb Viewing Hyper Spectral Imager (LiVHySI) for airglow measurements onboard YOUTHSAT-1

2014 ◽  
Vol 54 (3) ◽  
pp. 554-563 ◽  
Author(s):  
R.S. Bisht ◽  
A.K. Hait ◽  
P.N. Babu ◽  
S.S. Sarkar ◽  
A. Benerji ◽  
...  
Keyword(s):  
Hyper Spectral ◽  
Spectral Imager

Quality Assurance of Torrefied Biomass Using RGB, Visual and near Infrared (Hyper) Spectral Image Data

2014 ◽  
Vol 22 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Susanne Wiklund Lindström ◽  
David Nilsson ◽  
Anders Nordin ◽  
Martin Nordwaeger ◽  
Ingemar Olofsson ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Near Infrared ◽  
Image Data ◽  
Spectral Image ◽  
Hyper Spectral ◽  
Torrefied Biomass
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