Simulation of Moon-based Earth observation optical image processing methods for global change study

doi:10.1007/s11707-019-0749-9

Front. Earth Sci.

2020, Vol. 14

Issue (1) : 236-250 https://doi.org/10.1007/s11707-019-0749-9

RESEARCH ARTICLE

Simulation of Moon-based Earth observation optical image processing methods for global change study

Tong LI^1,²(

), Huadong GUO¹, Li ZHANG¹, Chenwei NIE^1,², Jingjuan LIAO¹, Guang LIU¹

¹. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
². University of Chinese Academy of Sciences, Beijing 100049, China

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Abstract

Global change affected by multiple factors, the consequences of which continue to be far-reaching, has the characteristics of large spatial scale and long-time scale. The demand for Earth observation technology has been increasing for large-scale simultaneous observations and stable global observation over the long-term. A Moon-based observation platform, which uses sensors on the nearside lunar surface, is considered a reasonable solution. However, owing to a lack of appropriate processing methods for optical sensor data, global change study using this platform is not sufficient. This paper proposes two optical sensor imaging processing methods for the Moon-based platform: area imaging processing method (AIPM) and global imaging processing method (GIPM), primarily considering global change characteristics, optical sensor performance, and motion law of the Moon-based platform. First, the study proposes a simulation theory which includes the construction of a Moon–Sun elevation angle model and a global image mosaicking method. Then, coverage images of both image processing methods are simulated, and their features are quantitatively analyzed. Finally, potential applications are discussed. Results show that AIPM, whose coverage is mainly affected by lunar revolution, is approximately between 0% and 50% with a period of 29.5 days, which can help the study of large-scale instant change phenomena. GIPM, whose coverage is affected by Earth revolution, is conducive to the study of long term global-scale phenomena because of its sustained stable observation from 67°N–67°S on the Earth. AIPM and GIPM have great advantages in Earth observation of tripolar regions. The existence of top of the atmosphere (TOA) albedo balance line is verified from the GIPM perspective. These two imaging methods play a significant role in linking observations acquired from the Moon-based platform to Earth large-scale geoscience phenomena, and thus lay a foundation for using this platform to capture global environmental changes and new discoveries.

Keywords Moon-based Earth observation, optical imaging processing method, global change remote sensing, simulation

Corresponding Author(s): Tong LI

Just Accepted Date: 20 March 2019 Online First Date: 12 August 2019 Issue Date: 24 March 2020

Cite this article:

Tong LI,Huadong GUO,Li ZHANG, et al. Simulation of Moon-based Earth observation optical image processing methods for global change study[J]. Front. Earth Sci., 2020, 14(1): 236-250.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0749-9
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I1/236

Fig.1 Sun-Earth-Moon rotation system. Regions B and C represent sunlit area. Region A and C are the observing area of the Moon-based optical sensor, which is approximately a hemisphere. Thus, the intersection region C represents the observational area by the Moon-based optical sensor.

Fig.2 Flow chart of simulated optical imaging processing methods’ cover area images. The first step is to prepare input data, including parameters such as the global Earth observation grids, experiment time, and Jet Propulsion Laboratory (JPL) Ephemerides. The second step is to obtain global-sun-elevation-angle-distribution and global-Moon-elevation-angle-distribution by inserting these parameters. AIPM’s cover area is the region where both elevation angles are greater than 0°. GIPM’s images are obtained through the global image mosaicking method.

Fig.3 Moon–Sun elevation angle model. The position of the Moon and the Sun in the Earth-centered inertial coordinate system can be looked up in JPL ephemeris data through Julian time converted from UTC. Then, the Earth-centered inertial coordinate system is converted into the Earth-centered Earth-fixed coordinate system. We establish a horizontal coordinate system based on every point which existed in a global observation grid. After that, the positions of Earth and the Moon in the Earth-centered Earth-fixed coordinate system are converted into the horizontal coordinate system and finally, elevation angles of the Sun and the Moon in the horizontal coordinate system are calculated.

Fig.4 Relationship of each coordinate system. Earth-centered Inertial coordinate system shown in red in this figure is used to obtain the position of the Sun and the Moon from JPL Ephemerides. Earth-centered Earth-fixed coordinate system with the orange line is used to link the Earth-centered inertial coordinate system and the horizontal coordinate system. The horizontal coordinate system depicted in green is used for elevation angle calculation.

Fig.5 Hourly AIPM’s coverage in experiment time. The font on the top of this figure depicts maximum coverage of each period, and the font on the bottom of this figure depicts the minimum coverage of each period.

Fig.6 AIPM’s coverage images at six AIPM maximum coverage times. The white area denotes AIPM’s cover area. The gradient blue shadow with time marked at the boundaries depicts different covered areas at different time.

Fig.7 Position change of AIPM coverage images’ latitude span. The space between the orange and blue line depicts the zonal covered area by AIPM. We also mark the Arctic circle line (67°N), the Antarctica circle line (67°S) and the Equator line (red line).

Fig.8 Daily GIPM’s coverage in experiment time. The curve line depicts the coverage at different times. The points on the picture depict the maximum and minimum coverages and their timings.

Fig.9 GIPM’s coverage images. The white area is GIPM’s cover area. The blue area is the area that could not be covered.

Fig.10 Position change of GIPM’s coverage images’ latitude span. The area between the yellow and blue line depicts the area covered by GIPM. We also marked the Arctic circle (67°N), the Antarctica circle (67°S), and the Equator (red line).

Fig.11 Comparison of AIPM’s coverage images and GIPM’s coverage images. Pictures (a), (c), and (e) indicate AIPM cover areas at different times within one day. Hourly AIPM coverage images filled in with different colors with time marked at the boundaries. Pictures (b), (d), and (f) indicate GIPM cover area in one day

Fig.12 Relationship between GIPM’s imaging frequency and AIPM’s coverage. The black line is GIPM’s imaging frequency. The blue line is AIPM’s coverage.

Tab.1 Temporal coverage of AIPM and GIPM for different spatial coverages in the tripolar regions

Fig.13 Mean value of TOA albedo in 2006–2017.

Fig.14 Position of the albedo balance line annually.

1	D Butler (2014). Earth observation enters next phase. Nature, 508(7495): 160–161 https://doi.org/10.1038/508160a
2	G R Carruthers, T Page (1972). Apollo 16 far-ultraviolet camera/spectrograph: earth observations. Science, 177(4051): 788–791 https://doi.org/10.1126/science.177.4051.788
3	E Chuvieco, J Li, X Yang (2010). Advances in earth observation of global change. Springer Netherlands, 35(6): 463–483 https://doi.org/10.1007/978-90-481-9085-0
4	P Civicioglu (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci, 46(3): 229–247 https://doi.org/10.1016/j.cageo.2011.12.011
5	A E Clarke, D Roy (2003). Astronomy Principles and Practice (PDF) (4th ed). Bristol: Institute of Physics Pub. p. 59. ISBN 9780750309172.
6	K Cui, J Xiang, Y Zhang (2017). Mission planning optimization of video satellite for ground multi-object staring imaging. Advances in Space Research, 2017, 61(6): 1476–1489
7	S Dewitte, S Nevens (2016). The total solar irradiance climate data record. Astrophys J, 830(1): 25 https://doi.org/10.3847/0004-637X/830/1/25
8	Y Ding, H Guo, G Liu (2014). Coverage performance analysis of earth observation from lunar base for global change detection. Journal of Hunan University, 41(10): 96–102 (in Chinese)
9	H Dong, X Zou (2014). Variations of sea ice in the antarctic and arctic from 1997–2006. Front Earth Sci, 8(3): 385–392 https://doi.org/10.1007/s11707-014-0422-2
10	W M Folkner, J G Williams, D H Boggs, R S Park, P Kuchynka (2014). The planetary and lunar ephemerides de430 and de431. IPN Progress Report, 196: 1–81
11	D Y Gao (2012). Global warming and ecology environment change in the three poles of Earth. Chinese Journal of Nature, 34(1): 18–23 (in Chinese)
12	H Guo, W Fu, X Li, P Cen, G Liu, Z Li, C Wang, Q Dong, L Lei, L Bai, Q Liu (2014). Research on global change scientific satellites. Sci China Earth Sci, 57(2): 204–215 (in Chinese) https://doi.org/10.1007/s11430-013-4748-5
13	H D Guo (2009). Space-based observation for sensitive factors of global change. Bulletin of the Chinese Academy of Sciences, 23(4): 226–228 (in Chinese)
14	H D Guo, Y X Ding, G Liu, D W Zhang, W X Fu, L Zhang (2013). Conceptual study of lunar-based SAR for global change monitoring. Sci China Earth Sci, 2013 https://doi.org/10.1007/s11430-013-4714-2
15	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/10.1080/17538947.2016.1264490
16	H D Guo, L W Zhu (2013). Earth observation for global change sensitive variables: mechanisms and methodologie. Bulletin of the Chinese Academy of Sciences, (4): 525–530
17	J Hansen, M Sato, P Kharecha, von K Schuckmann (2011). Earth energy imbalance and implications. Atmos Chem Phys, 11(9): 27031–27105 https://doi.org/10.5194/acpd-11-27031-2011
18	S Huang (2008). Surface temperatures at the nearside of the Moon as a record of the radiation budget of Earth climate system. Adv Space Res, 41(11): 1853–1860 https://doi.org/10.1016/j.asr.2007.04.093
19	A Jueterbock, I Smolina, J A Coyer, G Hoarau (2016). The fate of the arctic seaweed fucus distichus under climate change: an ecological niche modeling approach. Ecol Evol, 6(6): 1712–1724 https://doi.org/10.1002/ece3.2001
20	T Karalidi, D M Stam, F Snik, S Bagnulo, W B Sparks, C U Keller (2012). Observing the earth as an exoplanet with loupe, the lunar observatory for unresolved polarimetry of earth. Planet Space Sci, 74(1): 202–207 https://doi.org/10.1016/j.pss.2012.05.017
21	M Ligas, P Banasik (2011). Conversion between cartesian and geodetic coordinates on a rotational ellipsoid by solving a system of nonlinear equations. Geodesy and Cartography, 60(2): 145–159 https://doi.org/10.2478/v10277-012-0013-x
22	G Liu, H D Guo, X Y Wang, L Zhang (2016). Report on the Scheme and Key Technologies of Lunar Base Earth Observation. Strategic Pioneer Program on Space Science of Chinese Aeademy of Science
23	N G Loeb, B A Wielicki, F G Rose, D R Doelling (2007). Variability in global top-of-atmosphere shortwave radiation between 2000 and 2005. Geophys Res Lett, 34(3): L03704 https://doi.org/10.1029/2006GL028196
24	P Mouroulis, R O Green, T G Chrien (2000). Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information. Appl Opt, 39(13): 2210–2220 https://doi.org/10.1364/AO.39.002210
25	C W Nie, J J Liao, G Z Shen, W T Duan (2018). Simulation of the land surface temperature from moon-based earth observations. Adv Space Res, doi: 10.1016/j.asr.2018.09.041
26	E Pallé, P R Goode (2009). The lunar terrestrial observatory: observing the earth using photometers on the Moon’s surface. Adv Space Res, 43(7): 1083–1089 https://doi.org/10.1016/j.asr.2008.11.022
27	G Petit, B Luzum (2010). IERS conventions (2010). IERS Technical Notes, 36: 1–95
28	Y Z Ren, H D Guo, G Liu, H L Ye (2017). Simulation study of geometric characteristics and coverage for Moon-based Earth observation in the electro-optical region. IEEE J Sel Top Appl Earth Obs Remote Sens, 10(6): 2431–2440 https://doi.org/10.1109/JSTARS.2017.2711061
29	J Schombert (2011). Earth Coordinate System. University of Oregon Department of Physics.
30	W K Smith, W Gao, H Steltzer (2009). Current and future impacts of ultraviolet radiation on the terrestrial carbon balance. Front Earth Sci China, 3(1): 34–41 https://doi.org/10.1007/s11707-009-0011-y
31	G L Stephens, D O’Brien, P J, Webster P Pilewski, S Kato, J Li (2015). The albedo of Earth. Rev Geophys, 53(1): 141–163 https://doi.org/10.1002/2014RG000449
32	A Tsukamoto, W Kamisaka, H Senda, N Niisoe, H Aoki, T Otagaki (1996). High sensitivity pixel technology for a 1/4-inch PAL 430 k pixel IT-CCD. In: Proceedings of IEEE Custom Integrated Circuits Conference
33	A Voigt, B Stevens, J Bader, T Mauritsen (2013). The observed hemispheric symmetry in reflected shortwave irradiance. J Clim, 26(2): 468–477 https://doi.org/10.1175/JCLI-D-12-00132.1
34	M Wild (2009). Global environmental change. J Geophys Res D Atmospheres, 114(11): D00D16
35	J Wu (1999). Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Rem Sens, 25(4): 367–380 https://doi.org/10.1080/07038992.1999.10874736
36	Z Xu, K S Chen (2018). On signal modeling of Moon-based synthetic aperture radar (SAR) imaging of earth. Remote Sens, 10(3): 486 https://doi.org/10.3390/rs10030486
37	H L Ye, H D Guo, G Liu, Y Z Ren (2017). Observation scope and spatial coverage analysis for earth observation from a Moon-based platform. Int J Remote Sens: 1–25
38	H L Ye, H D Guo, G Liu, Y Z Ren (2018). Observation duration analysis for Earth surface features from a Moon-based platform. Adv Space Res, 62(2): 274–287 https://doi.org/10.1016/j.asr.2018.04.029
39	D W Zhang (2012). Study on methodology of lunar-based Earth observation for global change. Dissertation for the Master Degree. Shanghai: East China Normal University (in Chinese)
40	C Zhu, Zh Xie, F Li (2012). An Introduction to Global Change Science (3rd ed). Beijing: Science Press (in Chinese)

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