Applicability Analysis of Three Atmospheric Radiative Transfer Models in Nighttime

被引:0
|
作者
He, Jiacheng [1 ,2 ]
Zhang, Wenhao [1 ,2 ]
Liu, Sijia [1 ,2 ]
Zhang, Lili [3 ]
Liu, Qiyue [1 ,2 ]
Gu, Xingfa [1 ,3 ]
Yu, Tao [1 ,3 ]
机构
[1] North China Inst Aerosp Engn, Sch Remote Sensing & Informat Engn, Langfang 065000, Peoples R China
[2] Hebei Collaborat Innovat Ctr Aerosp Remote Sensing, Langfang 065000, Peoples R China
[3] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
关键词
radiative transfer model; SCIATRAN; MODTRAN; 6SV; VIIRS/DNB; nighttime; VICARIOUS CALIBRATION; PERFORMANCE; STABILITY; AEROSOL;
D O I
10.3390/atmos15010126
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] A linear stability analysis for nonlinear, grey, thermal radiative transfer problems
    Wollaber, Allan B.
    Larsen, Edward W.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2011, 230 (04) : 1528 - 1546
  • [32] Mapping of thermal power plant emitted atmospheric carbon dioxide concentration using AVIRIS-NG data and atmospheric radiative transfer model simulations
    Pandya, Mehul R.
    Chhabra, Abha
    Pathak, Vishal N.
    Trivedi, Himanshu
    Chauhan, Prakash
    JOURNAL OF APPLIED REMOTE SENSING, 2021, 15 (03)
  • [33] A Comparison of Observed and Simulated Brightness Temperatures from Two Radiative Transfer Models of RTTOV and CRTM
    Kim, Ju-Hye
    Kang, Jeon-Ho
    Lee, Sihye
    JOURNAL OF THE KOREAN EARTH SCIENCE SOCIETY, 2014, 35 (01): : 19 - 28
  • [34] Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data
    Hu, Pengcheng
    Zheng, Bangyou
    Chen, Qiaomin
    Grunefeld, Swaantje
    Choudhury, Malini Roy
    Fernandez, Javier
    Potgieter, Andries
    Chapman, Scott C.
    REMOTE SENSING OF ENVIRONMENT, 2024, 311
  • [35] Radiative Heat Transfer Analysis in Plasmonic Nanofluids for Direct Solar Thermal Absorption
    Lee, Bong Jae
    Park, Keunhan
    Walsh, Timothy
    Xu, Lina
    JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME, 2012, 134 (02):
  • [36] Development of a GPU-based high-performance radiative transfer model for the Infrared Atmospheric Sounding Interferometer (IASI)
    Huang, Bormin
    Mielikainen, Jarno
    Oh, Hyunjong
    Huang, Hung-Lung Allen
    JOURNAL OF COMPUTATIONAL PHYSICS, 2011, 230 (06) : 2207 - 2221
  • [37] Retrieval of fuel moisture content by using radiative transfer models from optical remote sensing data
    Quan X.
    He B.
    Liu X.
    Liao Z.
    Qiu S.
    Yin C.
    Yaogan Xuebao/Journal of Remote Sensing, 2019, 23 (01): : 62 - 77
  • [38] Use of semi-empirical and radiative transfer models to estimate biophysical parameters in a sparse canopy forest
    Boschetti, M
    Colombo, R
    Michele, M
    Busetto, L
    Panigada, C
    Brivio, PA
    Marino, CM
    Miller, JR
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY IV, 2003, 4879 : 133 - 144
  • [39] Why confining to vegetation indices? Exploiting the potential of improved spectral observations using radiative transfer models
    Atzberger, Clement
    Richter, Katja
    Vuolo, Francesco
    Darvishzadeh, Roshanak
    Schlerf, Martin
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XIII, 2011, 8174
  • [40] Neural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditions
    Hadjal, Madjid
    Paterson, Ross
    Mckee, David
    FRONTIERS IN REMOTE SENSING, 2023, 4