Cloud and aerosol polarimetric imager

Zhang, Junqiang; Jianbing, Shao; C, Yan

doi:10.1117/12.2054572

Cited by 7 publications

(2 citation statements)

References 1 publication

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“…This paper verified the possibility of using machine learning in modeling of polarization of light reflected by natural land surfaces. Moreover, although the GRNN-based BPDF model was validated by using the POLDER measurements, the four input parameters are quite straightforward and can be easily collected by any remote sensing radiometer, thus allowing us to extend the approach to further studies of polarized measurements of GF-5 [39], 3 MI [40], SGLI [41], and CAPI [42] etc. Table A1.…”

Section: Discussionmentioning

confidence: 99%

Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks

Yang

Lin

et al. 2020

Remote Sensing

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Retrieval of complete aerosol properties over land through remote sensing requires accurate information about the polarization characteristics of natural land surfaces. In this paper, a new bidirectional polarization distribution function (BPDF) is proposed, using the generalized regression neural network (GRNN). This GRNN-based BPDF model builds a quite accurate nonlinear relationship between polarized reflectance and four input parameters, i.e., Fresnel factor, scattering angle, red, and near-infrared reflectances. It learns fast because only a smoothing parameter needs to be adjusted. The GRNN-based model is compared to six widely used BPDF models (i.e., Nadal–Bréon, Maignan, Waquet, Litivinov, Diner, and Xie–Cheng models), using the Polarization and Directionality of the Earth’s Reflectance (POLDER) measurements. Experiments suggest that the GRNN-based BPDF model is more accurate than these models. Compared with the best current models, the averaged root-mean-square error (RMSE) from the GRNN-based BPDF model can be reduced by 13.4% by using data collected during the whole year and is lower for 97.4% cases with data collected during every month. Moreover, compared to the widely used BPDF models, the GRNN-based BPDF model provides better performance when the scattering angle is small, and it is the first model that is able to reproduce negative polarized reflectance. The GRNN-based BPDF model is thus useful for the remote sensing of complete aerosol properties over land.

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Section: Discussionmentioning

confidence: 99%

Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks

Yang

Lin

et al. 2020

Remote Sensing

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“…ACGS is a high spectral and spatial resolution grating spectrometer dedicated to measure the carbon dioxide and oxygen absorption spectra [7]. CAPI is designed to identify the disturbance by clouds and to detect the impact of aerosol within the instrument instantaneous field of view [8]. This paper addresses the prelaunch radiometric calibration of the ACGS performed during thermal vacuum testing and it is organized as following.…”

Section: Introductionmentioning

confidence: 99%

Preflight radiometric calibration of a carbon dioxide spectrometer

et al. 2019

Meas. Sci. Technol.

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The first Chinese Carbon Dioxide Observation Satellite (TanSat), which is designed to make high-precision, high-spatial resolution measurements of carbon dioxide, was successfully launched on 22 December 2016. There are two instruments installed on the TanSat: the High Resolution Hyper-Spectral Sensor for carbon observation Grating Spectrometer, aiming at measuring the amount of carbon dioxide (CO 2 ) in the atmosphere, and the cloud and aerosol polarimetric imager to identify the disturbance by clouds and to detect the impact of aerosol within the instrument instantaneous field of view. The two instruments work together to make global, high-precision CO 2 measurements with exactly correction of cloud and aerosol interference. The major content of this paper is the laboratory radiometric calibration of ACGS. Both the gain coefficients and the dark current response of each channel were determined during instrument-level ground testing. The gain coefficients for the ACGS were characterized by measuring the instrument response to illumination by an integrating sphere at several light levels. The dark response was calibrated with additional thermal balance testing. This paper also identified bad pixels, recorded in a table of data elements, to be omitted in science data processing. The uncertainties achieved during the radiometric calibration were below 5% (k = 1), meeting the absolute calibration mission requirements.

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Uncertainties of atmospheric polarimetric measurements with sun-sky radiometers induced by errors of relative orientations of polarizers

et al. 2018

Journal of Quantitative Spectroscopy and Radiative Transfer

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Cloud and aerosol polarimetric imager

Cited by 7 publications

References 1 publication

Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks

Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks

Preflight radiometric calibration of a carbon dioxide spectrometer

Uncertainties of atmospheric polarimetric measurements with sun-sky radiometers induced by errors of relative orientations of polarizers

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