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
  • [41] Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization
    Koetz, Benjamin
    Sun, Guoqing
    Morsdorf, Felix
    Ranson, K. J.
    Kneubuehler, Mathias
    Itten, Klaus
    Allgoewer, Britta
    REMOTE SENSING OF ENVIRONMENT, 2007, 106 (04) : 449 - 459
  • [42] Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models
    Guo, Binbin
    Zhang, Jing
    Xu, Tingbao
    Croke, Barry
    Jakeman, Anthony
    Song, Yongyu
    Yang, Qin
    Lei, Xiaohui
    Liao, Weihong
    WATER, 2018, 10 (11)
  • [43] A fast and accurate vector radiative transfer model for simulating the near-infrared hyperspectral scattering processes in clear atmospheric conditions
    Bai, Wenguang
    Zhang, Peng
    Zhang, Wenjian
    Ma, Gang
    Qi, Chengli
    Liu, Hui
    JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER, 2020, 242 (242)
  • [44] Estimation of photosynthetically available radiation (PAR) from OCEANSAT-I OCM using a simple atmospheric radiative transfer model
    Tripathy, Madhumita
    Raman, Mini
    Chauhan, Prakash
    ADVANCES IN SPACE RESEARCH, 2015, 56 (07) : 1441 - 1452
  • [45] Atmospheric water vapor radiative effects on shortwave radiation under clear skies: A global spatiotemporal analysis
    Salamalikis, Vasileios
    Vamvakas, Ioannis
    Gueymard, Christian A.
    Kazantzidis, Andreas
    ATMOSPHERIC RESEARCH, 2021, 251
  • [46] Modeling three-dimensional forest structures to drive canopy radiative transfer simulations of bidirectional reflectance factor
    Yang, Wei
    Kobayashi, Hideki
    Chen, Xuehong
    Nasahara, Kenlo Nishida
    Suzuki, Rikie
    Kondoh, Akihiko
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2018, 11 (10) : 981 - 1000
  • [47] Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models
    Atzberger, C
    REMOTE SENSING OF ENVIRONMENT, 2004, 93 (1-2) : 53 - 67
  • [48] Fast Calculation on Atmospheric Rayleigh Scattering Extinction Optical Depth for Day/Night Band Radiative Transfer Model With Periodic Lunar Illumination
    Liu, Shuyue
    Min, Min
    Di, Di
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2023, 128 (22)
  • [49] Estimation of wheat biophysical variables through UAV hyperspectral remote sensing using machine learning and radiative transfer models
    Sahoo, Rabi N.
    Rejith, R. G.
    Gakhar, Shalini
    Verrelst, Jochem
    Ranjan, Rajeev
    Kondraju, Tarun
    Meena, Mahesh C.
    Mukherjee, Joydeep
    Dass, Anchal
    Kumar, Sudhir
    Kumar, Mahesh
    Dhandapani, Raju
    Chinnusamy, Viswanathan
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 221
  • [50] Using radiative transfer models in order to estimate PM10 concentration in south of Iran using MODIS images
    Hojati, M.
    Boolorani, A. D.
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 2019, 16 (03) : 1405 - 1420