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Progress in monitoring high-temperature damage to rice through satellite and ground-based optical remote sensing

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Abstract

The occurrence of rice high-temperature damage (HTD) has increased with global warming. Cultivation of rice is seriously affected by the HTD in the middle and lower reaches of the Yangtze River, which directly affects food security in this region and in the whole of China. It is important to monitor and assess crop HTD using satellite remote sensing information. This paper reviews the recent development of monitoring rice HTD using optical remote sensing information. It includes the use of optical remote sensing information to obtain the regional spatial distribution of high temperatures, mixed-surface temperature retrieval for rice fields based on mixed decomposition information, the development of field and thermal infrared testing and modeling, and the satellite/ground-based remote sensing coupled method for monitoring rice HTD. Finally, the prospects for monitoring crop HTD based on remote sensing information are summarized.

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References

  1. IPCC. Climate Change 2007: Impacts, Adaptation and Vulnerability Summary for Policymakers Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2007

    Google Scholar 

  2. Rosenzweig C, Parry M L. Potential impact of climate-change on world food-supply. Nature, 1994, 367: 133–138

    Article  Google Scholar 

  3. Nicholls N. Increased Australian wheat yield due to recent climate trends. Nature, 1997, 387: 484–485

    Article  Google Scholar 

  4. Gifford R, Angus J, Barrett D, et al. Climate change and Australian wheat yield. Nature, 1998, 391: 448–449

    Article  Google Scholar 

  5. Lobell D B, Asner G P. Climate and management contributions to recent trends in US Agricultural Yield. Science, 2003, 299: 1032

    Article  Google Scholar 

  6. Tubiello F N, Soussana J F, Howden S M. Crop and pasture response to climate change. Proc Natl Acad Sci, 2007, 104: 19686–19690

    Article  Google Scholar 

  7. Chinese Academy of Agricultural Sciences. China Rice Cultivation (in Chinese). Beijing: China Agriculture Press, 1986

    Google Scholar 

  8. Chen S H, Li J. Model Chinese Rice (in Chinese). Beijing: Jindun Press, 2007

    Google Scholar 

  9. Yao F M, Zhang J H. Impact of Climate Change on Crop Yield and Its Simulation in China. Beijing: Meteorological Press, 2008

    Google Scholar 

  10. Gao L Z, Li L. Meteorological Ecology of Rice Crop (in Chinese). Beijing: China Agriculture Press, 1992

    Google Scholar 

  11. Peng S, Huang J, John E, et al. Rice yield decline with higher night temperature from global warming. Proc Natl Acad Sci, 2004, 101: 9971–9975

    Article  Google Scholar 

  12. Zhao H Y, Yao F M, Zhang Y, et al. Correlation analysis of rice seed setting rate and weight of 1000-grain and agro-meteorology over the middle and lower reaches of the Yangtze River. Sci Agri Sin, 2006, 39: 1765–1771

    Google Scholar 

  13. Yao F M, Xu Y L, Lin E D, et al. Assessing the impacts of climate change on rice yields in the main rice areas of China. Clim Change, 2007, 80: 395–409

    Article  Google Scholar 

  14. Xie X J, Li B B, Shen S H, et al. Influence of high temperature stress on some physiological characteristics of flag leaves of rice variety Yangdao 6 (in Chinese). Chin J Agrome, 2009, 30: 84–87

    Google Scholar 

  15. Chen L X, Zhu Q G, Luo H B, et al. East Asian Monsoon (in Chinese). Beijing: Meteorological Press, 1991. 162–192

    Google Scholar 

  16. Zhang J H, FU C B, Yao F M. Study on respondence of ecosystem to East Asian Monsoon in Eastern China using remote sensing data derived from remote sensing. Prog Nat Sci, 2004, 14: 279–283

    Article  Google Scholar 

  17. Yin J, Zhang C J, Zhang C M. Climatic diagnostic analysis of the exceptional high temperature in summer 2003 in Jiangxi Province (in Chinese). J Nanjing Inst Meterol, 2005, 28: 855–861

    Google Scholar 

  18. Yin D P, Yan M L, Pei H Y, et al. Synoptic character analysis on high temperature appearing under the control of subtropical high (in Chinese). Sci Meteorol Sin, 2006, 26: 559–563

    Google Scholar 

  19. Tian X H, Tsutomu M, Li S H, et al. High temperature stress on rice anthesis: Research progress and prospects (in Chinese). Chin J Appl Ecol, 2007, 18: 2632–2636

    Google Scholar 

  20. Wang J L, Chen X B. Progresses in rice heat tolerance (in Chinese). Hunai Agric Sci, 2006, 6: 23–26

    Google Scholar 

  21. Sun J L. Pandect on Dynamic Monitoring and Yield Estimation for Crop in China (in Chinese). Beijing: Chinese Science and Technique Press, 1996

    Google Scholar 

  22. Wang R C, Huang J F. Crop Yield Estimation Remote Sensing (in Chinese). Beijing: China Agriculture Press, 1996

    Google Scholar 

  23. Wang J H, Zhao C J, Huang W J, et al. Basis and Application of Agriculture Quantitative Remote Sensing (in Chinese). Beijing: Science Press, 2008

    Google Scholar 

  24. Price J C. Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. J Geophys Res, 1984, 89: 7231–7237

    Article  Google Scholar 

  25. Kerr Y H, Lagouarde J P, Imberon J. Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm. Remote Sens Environ, 1992, 41: 197–209

    Article  Google Scholar 

  26. Prata A J. Land surface temperature derived from the advanced very high resolution radiometer and the along-track scanning radiometer 1. Theory. J Geophys Res, 1993, 98: 689–702

    Google Scholar 

  27. Ulivieri C, Castronuovo M M, Francioni R, et al. A split window algorithm for estimating land surface temperature from satellites. Adv Space Res, 1994, 14: 59–65

    Article  Google Scholar 

  28. Norman J M, Divakarla M, Goel N S. Algorithms for extracting information from remote thermal-IR observations of the Earth’s surface. Remote Sens Environ, 1995, 51: 57–68

    Article  Google Scholar 

  29. Coll C, Caselles V. A split-window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison. J Geophys Res, 1997, 102: 16697–16713

    Article  Google Scholar 

  30. Qin Z, Karnieli A, Berliner P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int J Remote Sens, 2001, 22: 3719–3746

    Article  Google Scholar 

  31. Dash P, Gottsche F M, Olesen F S, et al. Land surface temperature and emissivity estimation from passive sensor data: Theory and practice current trends. Int J Remote Sens, 2002, 23: 2563–2594

    Article  Google Scholar 

  32. Jimenez-Munoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data. J Geophy Res, 2003, 108: 4688.ACL 2-1-ACL 2-9

    Article  Google Scholar 

  33. Sobrino J A, Jimenez-Munoz J C, Paolini L. Land surface temperature retrieval from LANDSAT TM5. Remote Sens Environ, 2004, 90: 434–440

    Article  Google Scholar 

  34. Li X W, Wang J F, Wang J D, et al. Multi-angle and Thermal Infrared Earth Remote Sensing (in Chinese). Beijing: Science Press, 2001

    Google Scholar 

  35. Tian G L. Thermal Infrared Remote Sensing (in Chinese). Beijing: Publishing House of Electronics Industry, 2006

    Google Scholar 

  36. Zhang J H, Hou Y Y, Li G C, et al. The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impacts based on remote sensing satellite data. Sci China Ser D-Earth Sci, 2005, 48(Suppl): 220–229

    Google Scholar 

  37. Tao J B, Yan J, Shi J Q. Crop heat temperature damage and its defensive measures (in Chinese). Anhui Agri Sci Bull, 2004, 10: 56–57

    Google Scholar 

  38. Yang C M, Heilnan J L. The effect of short-term high temperature on growth and yield (in Chinese). Foreign Crop Breeding, 1994, 2: 4–5

    Google Scholar 

  39. Cui D C. The scenario analysis of possible effect of warming climate on rice growing period (in Chinese). Q J Appl Meteorol, 1995, 6: 361–365

    Google Scholar 

  40. Yang X C, Lin R K, Wu Z H. Advances in rice heat temperature damage (in Chinese). Fujian Agric Sci Technol, 2006, 2: 68–69

    Google Scholar 

  41. Matsui T, Omasa K, Horie T. High temperature-induced spikelet sterility of japonica rice at flowering in relation to air temperature, humidity and wind velocity conditions. Jpn J Crop Sci, 1997, 66: 449–455

    Article  Google Scholar 

  42. Matsui T, Omasa K, Horie T. The difference in sterility due to high temperatures during the flowering period among japonica rice-varieties. Plant Prod Sci, 2001, 4: 90–93

    Article  Google Scholar 

  43. Matsui T, Kobayasi K, Yoshimoto M, et al. Stability of rice pollination in the field under hot and dry conditions in the Riverina Region of New South Wales, Australia. Plant Prod Sci, 2007, 10: 57–63

    Article  Google Scholar 

  44. Jiang Z Y, Huang Z Q, Yang H C, et al. The investigation about middle rice heat damage in the Jiang-Huai district in early 2003 (in Chinese). J Anhui Agric Sci, 2004, 32: 607–609

    Google Scholar 

  45. Gao S H, Wang P J. Study of Heat Temperature Damage in Middle and Lower Reaches of the Yangtze River (in Chinese). Beijing: Meteorological Press, 2009

    Google Scholar 

  46. Shanghai Institute of Plant Physiology Artificial Climatic Chamber. Effect of high temperature on early flowering and its prevention and control (in Chinese). Acta Bot Sin, 1977, 19: 126–130

    Google Scholar 

  47. Matsui T, Omasa K, Horie T. High temperature at flowering inhibits swelling of pollen grains, a driving force for thecae dehiscence in rice (Oryza sativa L.). Plant Prod Sci, 2000, 3: 430–434

    Article  Google Scholar 

  48. Xie X J, Li B B, Shen S H, et al. Effects of high temperature stress on pollen vitality and seed setting of rice cultivar during heading stage (in Chinese). Jiangsu J Agric Sci, 2009, 25: 238–241

    Google Scholar 

  49. Mo H D. Quality improvement of rice grain in China (in Chinese). Sci Agric Sin, 1993, 26: 8–14

    Google Scholar 

  50. Yoshida S, Saktake T, MacKill D S. High temperature stress in rice. IRRI Research Paper Series, No. 67. Manila: IRRI, 1981

    Google Scholar 

  51. Chauham J S, Moya T B, Singh R K, et al. Influence of soil moisture stress during reproductive stage on physiological parameters and grain yield in upland rice. Oryza, 1999, 36: 130–135

    Google Scholar 

  52. Garrity D P, O’Toole J C. Selection for reproductive stage drought avoidance in rice using infrared thermometry. Agron J, 1995, 87: 773–779

    Article  Google Scholar 

  53. Chen E, Allen L H, Bartholic J F, et al. Comparison of winter-noctual geostationary satellite infrared-surface temperature with shelter-height temperature in Florida. Remote Sens Environ, 1983, 13: 313–327

    Article  Google Scholar 

  54. Horiguchi I, Tani H, Motoki T. Accurate estimation of 1.5 m-height air temperature by GMS IR data. Proc 24th Int Cymp Rem Sens Environ Rio de Janeiro, May 1991. 301–307

  55. Green R. The potential of Pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications. Remote Sens Environ, 2002, 79: 166–175

    Article  Google Scholar 

  56. Nemani R R, Running S W. Estimation of resistance to evapotranspiration from NDVI and thermal-IR AVHRR data. J Clim Appl Meteor, 1989, 28: 176–294

    Article  Google Scholar 

  57. Saravanapavan T, Dye D G. Satellite estimation of environmental variables by the contextual analysis method: Validation in seasonal tropical environment. Global Engineering Laboratory Institute of Industrial Science University of Tokyo, Japan, 1995

  58. Yan H, Zhang J H, Hou Y Y, et al. Estimation of air temperature from MODIS data in east China. Inter J Remote Sens, 2009, 30: 6261–6275

    Article  Google Scholar 

  59. Hou Y Y, Zhang J H. Air temperature retrieval from remote sensing data at regional level (in Chinese). Meteorol Monthly, 2010, 36: 75–79

    Google Scholar 

  60. Zhang J H, Guo W J. Quantitative retrieval of crop water content under different soil moistures levels. Proc SPIE Int Soc Opt Eng, 2006

  61. Becker F, Li Z L. Temperature-independent spectral indices in thermal infrared bands. Remote Sens Environ, 1990, 32: 17–23

    Article  Google Scholar 

  62. Becker F, Li Z L. Surface temperature and emissivity at various scales: Definition, measurements and related problems. Remote Sens Rev, 1995, 12: 225–253

    Article  Google Scholar 

  63. Cresswell M P, Morse A P, Thomson M C, et al. Estimating surface air temperatures, from Meteosat land surface temperatures, using an empirical solar zenith angle model. Inter J Remote Sens, 1999, 20: 1125–1132

    Article  Google Scholar 

  64. Wan Z M, Li Z L. A physics-based algorithm for retrieving land-surface emissivity and temperature form Eos/MODIS data. IEEE Trans Geosci Remote Sens, 1997, 34: 892–905

    Google Scholar 

  65. Kenny G J, Harrison P A, Olesen J E, et al. The effects of climate change on land suitability of grain maize, winter wheat and cauliflower in Europe. Eur J Agron, 1993, 2: 325–338

    Google Scholar 

  66. Lambin E G, Ehrlich D. The surface temperature-vegetation index space for land cover and land-cover change analysis. Int J Remote Sens, 1996, 17: 463–487

    Article  Google Scholar 

  67. Goetz S Z. Multisensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. Int J Remote Sens, 1997, 18: 71–94

    Article  Google Scholar 

  68. Huete A R, Jackson R D. Soil and atmosphere influences on the spectra of partial canopies. Remote Sens Environ, 1988, 25: 89–105

    Article  Google Scholar 

  69. Nemani R R, Running S W. Estimation of regional surface-resistance to evapotranspiration from NDVI and thermal-IR AVHRR data. J Appl Meteorol, 1989, 28: 276–284

    Article  Google Scholar 

  70. Moran M S, Clarke T R, Kustas W P, et al. Evaluation of hydrologic parameters in a semiarid rangeland using remotely-sensed spectral data. Water Resour Res, 1994, 30: 1287–1297

    Article  Google Scholar 

  71. Gillies R R, Carlson T N, Cui J, et al. A verification of the’ triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature. Int J Remote Sens, 1997, 18: 3145–3166

    Article  Google Scholar 

  72. Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens Environ, 2002, 79: 213–224

    Article  Google Scholar 

  73. Tang Y L, Wang R C, Huang J F, et al. Hyperspectral data and their relationships correlative to the pigment contents for rice under different nitrogen support level (in Chinese). J Remote Sens, 2004, 8: 185–192

    Google Scholar 

  74. Chen G F, Zhang J H, Li B B, et al. Hyperspectral and red edge characteristics for rice under different temperature stress levels (in Chinese). Jiangsu J Agric Sci, 2008, 24: 573–580

    Google Scholar 

  75. Xie X J, Shen S H, Li Y X. Red edge characteristics and monitoring SPAD and LAI for rice with high temperature stress (in Chinese). Trans Chin Soc Agr Eng, 2010, 3: 183–190

    Google Scholar 

  76. Zheng J C, Sheng J, Tang R S, et al. Regularity of high temperature and its effects on pollen vigor and seed setting rate of rice in Nanjing and Anqing (in Chinese). Jiangsu J Agr Sci, 2007, 23: 1–4

    Google Scholar 

  77. Gao B C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ, 1996, 58: 257–266

    Article  Google Scholar 

  78. Tian Q J, Gong P, Zhao C J, et al. A feasibility study on diagnosing wheat water status using spectral reflectance. Chin Sci Bull, 2001, 46: 666–669

    Article  Google Scholar 

  79. Zarco-Tejada P J, Whiting M, Ustin S L. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agr J, 2005, 91: 641–653

    Article  Google Scholar 

  80. Zhang J H, Guo W J. Studying on spectral characteristics of winter wheat with different soil moisture condition. The 2nd International Symposium on Recent Advances in Quantitative Remote Sensing: RAQRS’II, Spain, 2006

  81. Ghulam A, Li Z L, Qin Q M, et al. A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index. Sci China Ser D-Earth Sci, 2007, 50: 957–965

    Article  Google Scholar 

  82. Cheng Y B, Zarco-Tejada P J, Riaño D, et al. Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes. Remote Sens Environ, 2006, 105: 354–366

    Article  Google Scholar 

  83. Chen G F. Study on the physiological and hyperspectral characteristics of rice under high temperature stress. Dissertation for the Master Degree. Nanjing: Jiangsu University/Chinese Academy of Meteorological Sciences, 2009

    Google Scholar 

  84. Zhang J H, Fu C B. A study on relationships between remote sensing information and plant photosynthetic parameters in estimating biomass model (in Chinese). Acta Geol Cartogr Sin, 1999, 28: 128–132

    Google Scholar 

  85. Ren C F. The effect of high temperature on the physiology and biochemical foundation of flowering and fertilization of hybrid rice (in Chinese). J Southwest Agric Univ, 1990, 12: 440–443

    Google Scholar 

  86. Xu D Q, Shen R G. Restrained Factors of Photosynthesis-Plant Physiology and Molecular Biology. Beijing: Science Press, 1998. 262–276

    Google Scholar 

  87. Wang C Y, Zhu Y J, Xia G J, et al. Changes of photosynthetic parameters in wheat flag leaf and their correlation analysis under post-anthesis high temperature conditions (in Chinese). Acta Agric Boreali-Sin, 2003, 18: 8–11

    Google Scholar 

  88. Zhang G L, Chen L Y, Zhang S, et al. Effects of high temperature on physiological and biochemical characteristics in flag leaf of rice during heading and flowering period (in Chinese). Sci Agric Sin, 2007, 20: 1345–1352

    Google Scholar 

  89. Xie X J, Li B B, Chen G F. Effects of high temperature on physiological characteristics in flag leaves of different rice (in Chinese). Res Agric Modern, 2009, 30: 484–486

    Google Scholar 

  90. Yasuyuki W. Infrared remote sensing for canopy temperature in paddy field and relationship between leaf temperature and leaf color. J Agric Meteorol, 2002, 58: 185–194

    Article  Google Scholar 

  91. Guo P G, Li R H. Effects of high nocturnal temperature on photosynthetic organization in rice leaves (in Chinese). Acta Bot Sin, 2000, 42: 673–678

    Google Scholar 

  92. Teng Z H, Zhi L, Zong X F, et al. Effects of high temperature on chlorophyll fluorescence, active oxygen resistance activity, and grain quality in grain-filling periods in rice plants (in Chinese). Acta Agron Sin, 2008, 34: 162–166

    Google Scholar 

  93. Carter G A, Freedman A, Kebabian P L, et al. Use of a prototype instrument to detect short-term changes in solar-excited fluorescence. Int J Remote Sens, 2004, 25: 1779–1784

    Article  Google Scholar 

  94. Liu L Y, Zhang Y J, Wang J H, et al. Detecting photosynthesis fluorescence under natural sunlight based on Fraunhofer line. J Remote Sen, 2006, 10: 147–154

    Google Scholar 

  95. Zhang Y J, Liu L Y, Wang J H, et al. Detection of leaf fluorescence from reflectance using hyper-spectrometer (in Chinese). Opt Tech, 2007, 33: 119–123

    Google Scholar 

  96. Campbell P K, Middleton E M, Corp L A, et al. Contribution of chlorophyll fluorescence to the apparent vegetation reflectance. Sci Total Environ, 2008, 404: 433–439

    Article  Google Scholar 

  97. Li X W, Wang J D, Strahler A H. Scale effects of Planck law over non-isothermal blackbody surface. Sci China Ser E-Eng Mater Sci, 1999, 42: 652–656

    Article  Google Scholar 

  98. Chen L F, Zhuang J L, Li Q H, et al. Study on the law of radiant directionality of row crops. Sci China Ser E-Eng Mater Sci, 2000, 43(Supp.): 70–82

    Article  Google Scholar 

  99. Fan W J, Xu X R. The correlation of multi-angle thermal infrared data and the choice of optimal view angles, Sci China Ser D-Earth Sci, 2004, 47: 570–576

    Article  Google Scholar 

  100. McFarland M J, Miller R L, Neale C M U. Land surface temperature derived from the SSM/I Passive Microwave brightness temperatures. IEEE Trans Geosci Remote Sensing, 1990, 28: 839–845

    Article  Google Scholar 

  101. Pulliainen J T, Grandell J, Hallikainen M T. Retrieval of surface temperature in boreal forest zone from SSM/I data. IEEE Trans Geosci Remote Sensing, 1997, 35: 1188–1200

    Article  Google Scholar 

  102. Weng F Z, Grody N C. Physical retrieval of land surface temperature using the special sensor microwave imager. J Geophy Res-Atmos, 1998, 103: 8839–8848

    Article  Google Scholar 

  103. Basist A, Grody N C, Peterson T C, et al. Using the special sensor microwave/imager to monitor land surface temperatures, wetness, and snow cover. J Appl Meteorol, 1998, 37: 888–911

    Article  Google Scholar 

  104. Goita K, Royer A. Combination of passive microwave and thermal infrared for the retrieval and analysis of microwave emissivities and temperature. IEEE Inter Geosci Remote Sens Sym, 2002, 4: 2401–2403

    Google Scholar 

  105. Fily M, Royer A, Goita K, et al. A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas. Remote Sens Environ, 2003, 85: 328–338

    Article  Google Scholar 

  106. Brodzik M J. CLPX-Satellite: AMSR-E Brightness Temperature Grids. Boulder, CO: National Snow and Ice Data Center Digital Media, 2003

    Google Scholar 

  107. Gao H L, Fu R, Dickinson R E, et al. A practical method for retrieving land surface temperature from AMSR-E over the Amazon forest. IEEE Trans Geosci Remote Sensing, 2008, 46: 193–199

    Article  Google Scholar 

  108. Jia Y Y, Li Z L. Progress in land surface temperature retrieval from passive microwave remotely sensed data (in Chinese). Prog Geogr, 2006, 25: 96–105

    Google Scholar 

  109. Ji Y, Stocker E. An overview of the TRMM/TSDIS fire algorithm and products. Int J Remote Sens, 2002, 23: 3285–3303

    Article  Google Scholar 

  110. Went J, Su Z B, Ma Y M. Determination of land surface temperature and soil moisture from Tropical Rainfall Measuring Mission/Micro-wave Imager remote sensing data. J Geophy Res, 2003, 108: ACL2.1–ACL2.10

    Google Scholar 

  111. Corbella I, Torres F, Camps A, et al. Brightness-temperature retrieval methods in synthetic aperture radiometers. IEEE Trans Geosci Remote Sensing, 2009, 47: 285–294

    Article  Google Scholar 

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Zhang, J., Yao, F., Li, B. et al. Progress in monitoring high-temperature damage to rice through satellite and ground-based optical remote sensing. Sci. China Earth Sci. 54, 1801–1811 (2011). https://doi.org/10.1007/s11430-011-4210-5

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