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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Front. Earth Sci.    2020, Vol. 14 Issue (2) : 430-445    https://doi.org/10.1007/s11707-019-0785-5
RESEARCH ARTICLE
Exploring the influence of various factors on microwave radiation image simulation for Moon-based Earth observation
Linan YUAN1,2, Jingjuan LIAO1()
1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

Earth observation technologies are important for obtaining geospatial information on the Earth’s surface and are used widely in many disciplines, such as resource surveying, environmental monitoring, and evolutionary studies. However, it is a challenge for existing Earth observation platforms to acquire this type of data rapidly on a global scale due to limitations in orbital altitude and field of view; thus development of an advanced platform for Earth observation is desirable. As a natural satellite of the Earth, placement of various sensors on the Moon could possibly facilitate comprehensive, continuous, and long-term observations of the Earth. This is a relatively new concept and the study is still at the preliminary stage with no actual Moon-based Earth observation data available at this time. To understand the characteristics of Moon-based microwave radiation, several physical factors that potentially influence microwave radiation imaging, e.g., time zone correction, relative movement of the Earth-Moon, atmospheric radiative transfer, and the effect of the ionosphere, were examined. Based on comprehensive analysis of these factors, the Moon-based microwave brightness temperature images were simulated using spaceborne temperature data. The results show that time zone correction ensures that the simulation images may be obtained at Coordinated Universal Time (UTC) and that the relative movement of the Earth-Moon affects the positions of the nadir and Moon-based imaging. The effect of the atmosphere on Moon-based observation is dependent on various parameters, such as atmospheric pressure, temperature, humidity, water vapor, carbon dioxide, oxygen, the viewing zenith angle and microwave frequency. These factors have an effect on atmospheric transmittance and propagation of upward and downward radiation. When microwaves propagate through the ionosphere, the attenuation is related to frequency and viewing zenith angle. Based on initial studies, the simulation results suggest Moon-based microwave radiation imaging is realistic and viable.

Keywords Moon-based Earth observation      microwave brightness temperature simulation      relative movement of Earth-Moon      atmospheric radiative transfer      ionosphere     
Corresponding Author(s): Jingjuan LIAO   
Just Accepted Date: 21 November 2019   Online First Date: 19 December 2019    Issue Date: 21 July 2020
 Cite this article:   
Linan YUAN,Jingjuan LIAO. Exploring the influence of various factors on microwave radiation image simulation for Moon-based Earth observation[J]. Front. Earth Sci., 2020, 14(2): 430-445.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0785-5
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I2/430
Fig.1  The relative position between the Earth and the Moon.
Fig.2  (a) Atmospheric attenuation caused by oxygen and water vapor at 0–1000 GHz. (b) The change of atmospheric attenuation with altitude at different frequencies.
Fig.3  Distribution of global LST before time zone correction.
Fig.4  Distribution of global LST after time zone correction.
Fig.5  The fitting results of the LST and MODIS LST data for the four MODIS pixels. (a) South America (b) Oceania (c) Asia and Europe (d) Africa.
Fig.6  The Moon-based imaging coverage and zenith angle distribution at different times on January 1, 2005
Fig.7  The change of atmospheric transmittance and upward radiation with atmospheric humidity, temperature, viewing zenith angle, and frequency
Fig.8  (a) The change in attenuation of microwave energy with collision frequency in the ionosphere. (b) The change of attenuation of microwave energy with microwave frequency in the ionosphere.
Fig.9  Comparison of the effect of the ionosphere on Moon-based microwave radiation brightness temperatures at different frequencies.
Fig.10  The images for simulation of the Moon-based microwave radiation brightness temperatures at UTC 08:00 on January 1, 2005.
Fig.11  The images for simulation of the Moon-based microwave radiation brightness temperatures at UTC 12:00 on January 1, 2005.
Fig.12  The comparison of the brightness temperatures for the Moon-based simulation and the AMSR-E microwave radiometer.
Fig.13  The root mean square error (RMSE) of the Moon-based simulation images for the different continents.
Number T0 /K Ta/K α β
1 234.647 7.34024 -1.01117 1.0071
2 243.706 5.25396 -5.68419 0.40823
3 286.887 15.0345 -3.12953 0.441291
4 226.487 47.8038 -1.32001 1.20496
5 260.121 6.91076 -1.78134 1.12404
6 256.971 3.16141 -2.92833 1.21333
7 279.69 28.5639 -6.18648 0.454205
8 292.803 10.9054 0.099393 0.277346
9 293.764 9.25083 -0.19969 -0.33375
10 287.412 7.90905 -0.71262 0.365055
11 291.317 12.813 -0.55321 0.39392
12 222.112 24.5167 0.22719 0.965652
13 234.647 7.34024 -1.01117 1.0071
  Table A1 The fitting results for parameters of the diurnal temperature cycle model
Frequency/GHz 6.9 GHz 10.8 GHz 18.7 GHz 23.8GHz 36.5 GHz 89.0 GHz
Land surface Average error/K 5.29 6.32 4.96 3.34 3.68 1.36
RMSE/K 1.10 1.51 1.55 1.52 1.53 1.72
Relative error 3.11% 3.59% 2.47% 1.45% 1.64% 0.51%
Sea surface Average error/K 1.02 1.45 1.63 1.61 2.05 2.70
RMSE/K 0.10 0.18 0.43 0.73 0.50 0.42
Relative error 0.40% 0.57% 0.65% 0.64% 0.83% 1.13%
  Table A2 Comparison of accuracy for Moon-based microwave radiation image simulation
1 S O Alsweiss, Z Jelenak, P S Chang (2017). Remote sensing of sea surface temperature using AMSR-2 measurements. IEEE J-STARS, 10(9): 3948–3954
https://doi.org/10.1109/JSTARS.2017.2737470
2 G Carella, J Kennedy, D I Berry, S Hirahara, C J Merchant, S Morak-Bozzo, E C Kent (2018). Estimating sea surface temperature measurement methods using characteristic differences in the diurnal cycle. Geophys Res Lett, 45(1): 363–371
https://doi.org/10.1002/2017GL076475
3 C Cong, C Shi, Z Shi (2018). A comparative study on electromagnetic wave propagation in a plasma sheath based on double parabolic model. Optik (Stuttg), 159: 69–78
https://doi.org/10.1016/j.ijleo.2017.12.078
4 M I Devi, I Khan, D N M Rao (2008). A study of VLF wave propagation characteristics in the earth-ionosphere waveguide. Earth Planets Space, 60(7): 737–741
https://doi.org/10.1186/BF03352822
5 Y Ding, H Guo, G Liu (2014). Potential applications of the moon based synthetic aperture radar for earth observation. IEEE Int Geosci Remote Sens Symp: 1767–1769
6 S B Duan, Z L Li, B H Tang, H Wu, R Tang (2014). Direct estimation of land-surface diurnal temperature cycle model parameters from MSG-SEVIRI brightness temperatures under clear sky conditions. Remote Sens Environ, 150: 34–43
https://doi.org/10.1016/j.rse.2014.04.017
7 S B Duan, Z L Li, N Wang, H Wu, B H Tang (2012). Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data. Remote Sens Environ, 124: 15–25
https://doi.org/10.1016/j.rse.2012.04.016
8 G Fornaro, G Franceschetti, F Lombardini, A Mori, M Calamia (2010). Potentials and limitations of moon-borne SAR imaging. IEEE T Geosci Remote, 48(7): 3009–3019
https://doi.org/10.1109/TGRS.2010.2041463
9 C L Gentemann (2003). Diurnal signals in satellite sea surface temperature measurements. Geophys Res Lett, 30(3): 1140
https://doi.org/10.1029/2002GL016291
10 H D Guo, Y X Ding, G Liu, D W Zhang, W X Fu, L Zhang (2014). Conceptual study of lunar-based SAR for global change monitoring. Sci China Earth Sci, 57(8): 1771–1779
https://doi.org/10.1007/s11430-013-4714-2
11 H D Guo, G Liu, Y X Ding (2018). Moon-based Earth observation: scientific concept and potential applications. Int J Digit Earth, 11(6): 546–557
https://doi.org/10.1080/17538947.2017.1356879
12 H Guo, G Liu, Y Ding, Y Zou, S Huang, L Jiang, J Gensuo, M Lv, Y Ren, Z Ruan, H Ye (2016). Moon-based earth observation for large scale geoscience phenomena. IEEE Int Geosci Remote Sens Symp: 546–557
13 P Hamill (2007). Atmospheric observations from the moon: a lunar earth-observatory. In: NASA advisory council workshop on science associated with the lunar exploration architecture white papers
14 Q L Huang, Z Y Zhang, W Guo (2001). Approach to generate radiometric images. Int J Infrared Milli, 22(12): 1805–1811
https://doi.org/10.1023/A:1015075617748
15 S Huang (2004). Merging information from different resources for new insights into climate change in the past and future. Geophys Res Lett, 31(13)
https://doi.org/10.1029/2004GL019781
16 S Huang (2008). Surface temperatures at the nearside of the moon as a record of the radiation budget of Earth’s climate system. Adv Space Res, 41(11): 1853–1860
https://doi.org/10.1016/j.asr.2007.04.093
17 J R Johnson, P G Lucey, T C Stone, M I Staid (2007). Visible/near-infrared remote sensing of Earth from the moon. In: NASA advisory council workshop on science associated with the lunar exploration architecture white papers.
18 M Laroussi, J R Roth (1993). Numerical calculation of the reflection, absorption, and transmission of microwaves by a nonuniform plasma slab. IEEE Trans Plasma Sci, 21(4): 366–372
https://doi.org/10.1109/27.234562
19 Y Ren, H Guo, G Liu, H Ye (2017). Simulation study of geometric characteristics and coverage for Moon-based earth observation in the electro-optical region. IEEE J STARS, 10(6): 2431–2440
https://doi.org/10.1109/JSTARS.2017.2711061
20 N A Salmon (2004). Polarimetric scene simulation in millimeter-wave radiometric imaging. Proc SPIE Int Soc Opt Eng, 5410(7): 260–269
21 N A Salmon (2018). Outdoor passive millimeter wave imaging: phenomenology and scene simulation. IEEE Trans Antenn Propag, 66(2): 897–908
https://doi.org/10.1109/TAP.2017.2781742
22 S Schädlich, F M Göttsche, F S Olesen (2001). Influence of land surface parameters and atmosphere on METEOSAT brightness temperatures and generation of LST maps by temporally and spatially interpolating atmospheric correction. Remote Sens Environ, 75(1): 39–46
https://doi.org/10.1016/S0034-4257(00)00154-1
23 A R L Tatnall, R P Donnelly, J E C Charlton (1996). Microwave radiometer model simulation. Int J Remote Sens, 17(16): 3107–3120
https://doi.org/10.1080/01431169608949133
24 X H Wang, H Zhang (2017). Effects of Australian summer monsoon on sea surface temperature diurnal variation over the Australian north-western shelf. Geophys Res Lett, 44(19): 9856–9864
https://doi.org/10.1002/2017GL075008
25 X Yang, Z Song, Y H Tseng, F Qiao, Q Shu (2017). Evaluation of three temperature profiles of a sublayer scheme to simulate SST diurnal cycle in a global ocean general circulation model. J Adv Model Earth Syst, 9(4): 1994–2006
https://doi.org/10.1002/2017MS000927
26 H Ye, H Guo, G Liu, Y Ren, Y Ding, M Lv (2016). Coverage analysis on global change sensitive regions from lunar based observation. IEEE Int Geosci Remote Sens Symp: 3734–3737
27 C Yu 2012. Study on characteristics of electromagnetic waves propagating through ionosphere and microwave absorbing properties of carbonaceous materials. Dissertation for PhD Degree. Nanjing: Nanjing University of Information Science and Technology (in Chinese)
28 L Yujiri, S W Fornaca, B I Hauss, R T Kuroda, R Lai, M Shoucri (1999). 140-GHz passive millimeter-wave video camera. Proc SPIE, 3364: 20–27
https://doi.org/10.1117/12.353006
29 L Yujiri, M Shoucri, P Moffa (2003). Passive millimeter wave imaging. IEEE Microw Mag, 4(3): 39–50
https://doi.org/10.1109/MMW.2003.1237476
30 C Zhang, J Wu (2007). Image simulation for ground objects microwave radiation. J Electr Inf Technol, 12(4): 750–764 (in Chinese)
31 D W Zhang 2012. Study on Moon-Earth observation methodology for global change Dissertation for PhD Degree. Shanghai: East China Normal University (in Chinese)
32 G Zhang, Z Zhang, W Guo (2003). 8 mm radiometric simulation detection based on optical image. Int J Infrared Millim Waves, 24(4): 603–611
https://doi.org/10.1023/A:1022467201427
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