Skip to main content
Log in

MODIS-based air temperature estimation in the southeastern Tibetan Plateau and neighboring areas

  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

Abstract

Climatic conditions are difficult to obtain in high mountain regions due to few meteorological stations and, if any, their poorly representative location designed for convenient operation. Fortunately, it has been shown that remote sensing data could be used to estimate near-surface air temperature (Ta) and other climatic conditions. This paper makes use of recorded meteorological data and MODIS data on land surface temperature (Ts) to estimate monthly mean air temperatures in the southeastern Tibetan Plateau and its neighboring areas. A total of 72 weather stations and 84 MODIS images for seven years (2001 to 2007) are used for analysis. Regression analysis and spatio-temporal analysis of monthly mean Ts vs. monthly mean Ta are carried out, showing that recorded Ta is closely related to MODIS Ts in the study region. The regression analysis of monthly mean Ts vs. Ta for every month of all stations shows that monthly mean Ts can be rather accurately used to estimate monthly mean Ta (R2 ranging from 0.62 to 0.90 and standard error between 2.25°C and 3.23°C). Thirdly, the retrieved monthly mean Ta for the whole study area varies between 1.62°C (in January, the coldest month) and 17.29 °C (in July, the warmest month), and for the warm season (May–September), it is from 13.1°C to 17.29°C. Finally, the elevation of isotherms is higher in the central mountain ranges than in the outer margins; the 0°C isotherm occurs at elevation of about 4500±500 m in October, dropping to 3500±500 m in January, and ascending back to 4500±500 m in May next year. This clearly shows that MODIS Ts data combining with observed data could be used to rather accurately estimate air temperature in mountain regions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anderson M C, Norman J M, Kustas W P et al., 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112(12): 4227–4241.

    Article  Google Scholar 

  • Anderson S, 2002. An evaluation of spatial interpolation methods on air temperature in Phoenix, Arizona State. Department of Geography, Arizona State University. Retrieved from the World Wide Web: http://www.cobblestoneconcepts.com/ucgis2summer/anderson/anderson.htm.

  • Barry R G, 1992. Mountain Weather and Climate. London and New York: Routledge.

    Google Scholar 

  • Barry R G, 2008. Mountain Weather and Climate. Boulder, USA: University of Colorado.

    Book  Google Scholar 

  • Brunsdon C, Fotheringham A S, Charlton M, 1996. Geographically weighted regression: A method for exploring spatial non-stationarity. Geographical Analysis, 28(4): 281–298.

    Article  Google Scholar 

  • Carlson Toby N, Buffum Martha J, 1989. On estimating total daily evapotranspiration from remote surface temperature measurements. Remote Sensing of Environment, 29(2): 197–207.

    Article  Google Scholar 

  • Connor S J, Thomson M C, Flasse S P et al., 1998. Environmental information systems in malaria risk mapping and epidemic forecasting. Disasters, 22(1): 39–56.

    Article  Google Scholar 

  • Erdogan S, 2010. Modelling the spatial distribution of DEM error with geographically weighted regression: An experimental study. Computers & Geosciences, 36: 34–43.

    Article  Google Scholar 

  • Florio E N, Lele S R, Chang Y C et al., 2004. Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: A statistical approach. International Journal of Remote Sensing, 25(15): 2979–2994.

    Article  Google Scholar 

  • Focks D A, Daniels E, Haile D G et al., 1995. A simulation-model of the epidemiology of urban dengue fever: Literature analysis, model development, preliminary validation, and samples of simulation results. American Journal of Tropical Medicine and Hygiene, 53(5): 489–506.

    Google Scholar 

  • Fotheringham A S, Brunsdon C, Charlton M E, 2002. Geographically Weighted Regression. New York: Wiley.

    Google Scholar 

  • Fu Baopu, 1983. Mountain Weather. Beijing: Science Press. (in Chinese)

    Google Scholar 

  • Goetz S J, Prince S D, Small J, 2000. Advances in satellite remote sensing of environmental variables for epidemiological applications. Advances in Parasitology, 47: 289–307.

    Article  Google Scholar 

  • Guerschman J P, Van Dijk A I J M, Mattersdorf G et al., 2009. Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. Journal of Hydrology, 369(1/2): 107–119.

    Article  Google Scholar 

  • Hao Zhenchun, Jiang Weijuan, Ju Qin et al., 2010. The features of climate changes in the five river source regions of the Tibetan Plateau. Journal of Glaciology and Geocryology, 32(6): 1130–1136. (in Chinese)

    Google Scholar 

  • He Yunling, Zhang Yiping, 2004. The climate characteristics and change trends on basins of Lancangjiang Valley in Yunnan Province. Journal of Mountain Science, 22(5): 539–548. (in Chinese)

    Google Scholar 

  • Holtmeier F-K, 2003. Mountain Timberlines: Ecology, Patchiness, and Dynamics. Dordrecht: Boston, Kluwer Academic Publishers.

    Google Scholar 

  • IPCC, 2001. Climate Change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom: Cambridge University Press. Retrieved from the World Wide Web: http://www.grida.no/publications/other/ipcc_tar/: 881.

    Google Scholar 

  • Jin M, Dickinson R E, 2000. A generalized algorithm for retrieving cloudy sky skin temperature from satellite thermal infrared radiances. Journal of Geophysical Research, 105(22): 27037–27047.

    Article  Google Scholar 

  • Jones P, Jedlovec G, Suggs R et al., 2004. Using MODIS LST to estimate minimum air temperatures at night. In: The 13th Conference on Satellite Meteorology and Oceanography. Norfolk, VA: AMS 4.13. Retrieved From the World Wide Web: http://ams.confex.com/ams/pdfpapers/79017.pdf.

    Google Scholar 

  • Li Zongxing, He Yuanqing, Xin Huijuan et al., 2010. Spatio-temporal variations of temperature and precipitation in Mts. Hengduan Region during 1960–2008. Acta Geographica Sinica, 65(5): 563–579. (in Chinese)

    Google Scholar 

  • Liu Lunhui, Yu Youde, Zhang Jianhua, 1984. The division of vertical vegetation zone in Hengduan Mountains. Acta Botanica Yunnanica, 6(2): 205–216. (in Chinese)

    Google Scholar 

  • Liu Lunhui, Yu Youde, Zhang Jianhua, 1985. Discussion upon the regularities of vegetational distribution in the Hengduan Mountains. Acta Botanica Yunnanica, 7(3): 323–325. (in Chinese)

    Google Scholar 

  • Meng Xiangfei, Wei Hong, Xie Xiaohong et al., 2010. Study of the spatial distribution of land surface temperature of Yunnan Province based on MODIS data. Journal of Southwest University: Natural Science Edition, 32(12): 154–158. (in Chinese)

    Google Scholar 

  • Miehe G, Miehe S, Vogel J et al., 2007. Highest treeline in the Northern Hemisphere found in Southern Tibet. Mountain Research and Development, 27(2): 169–173.

    Article  Google Scholar 

  • Prihodko L, Goward S N, 1997. Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment, 60(3): 335–346.

    Article  Google Scholar 

  • Prince S D, Goetz S J, Dubayah R O et al., 1998. Inference of surface and air temperature, atmospheric precipitable water and vapor pressure deficit using advanced very high-resolution radiometer satellite observations: Comparison with field observations. Journal of Hydrology, 213(1–4): 230–249.

    Article  Google Scholar 

  • Propastin P, Kappas M, Erasmi S, 2008. Application of geographically weighted regression to investigate the impact of scale on prediction uncertainty by modelling relationship between vegetation and climate. International Journal of Spatial Data Infrastructures Research, 3: 73–94.

    Google Scholar 

  • Quervain A de, 1904. Die Hebung der atmosphärischen lsothermenin der Schweizer Alpen und ihre Beziehung zu deren Höhengrenzen. Gerlands Beitr. Geophys., 6: 481–533.

    Google Scholar 

  • Schweinfurth U, 1972. The eastern marches of high Asia and the river gorge country. In: Troll C (ed.). Geoecology of the high-mountain regions of Eurasia. Wiesbaden: Franz Steiner Verlag GmbH.

    Google Scholar 

  • Shu Shoujuan, Wang Yuan, Chu Huiyun, 2009. Spatial distribution of temperature in China: Geographic and orographic influences. Journal of Nanjing University (Natural Sciences), 45(3): 334–342. (in Chinese)

    Google Scholar 

  • Shu Yunqiao, Stisen Simon, Jensen Karsten H et al., 2011. Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data. International Journal of Applied Earth Observation and Geoinformation, 13(2): 192–206. (in Chinese)

    Article  Google Scholar 

  • Smith W L, Leslie L M, Diak G R et al., 1988. The integration of meteorological satellite imagery and numerical dynamical forecast models. Philosophical Transactions Royal Society of London, 324: 317–323.

    Google Scholar 

  • Stisen S, Sandholt I, Norgaard A et al., 2007. Estimation of diurnal air temperature using MSG SEVIRI data in West Africa. Remote Sensing of Environment, 110(2): 262–274.

    Article  Google Scholar 

  • Troll C, 1973. The upper timberlines in different climatic zones. Arctic and Alpine Research, 5(3): 3–18.

    Google Scholar 

  • Vancutsem C, Ceccato P, Dinku T et al., 2009. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114: 449–465.

    Article  Google Scholar 

  • Vogt J, Viau A A, Paquet F, 1997. Mapping regional air temperature fields using satellite derived surface skin temperatures. International Journal of Climatology, 17: 1559–1579.

    Article  Google Scholar 

  • Wan Yunxia, Zhang Wancheng, Xiao Ziniu, 2009. Spatiotemporal variation characteristics of air temperature in longitudinal ridge-gorge region of Yunnan in recent century. Journal of Natural Disasters, 18(5): 183–189. (in Chinese)

    Google Scholar 

  • Willmott C J, Robeson S M, 1995. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology, 15: 221–229.

    Article  Google Scholar 

  • Willmott C J, Robeson S M, Feddema J J, 1991. Influence of spatially variable instrument networks on climatic averages. Geophysical Research Letter, 18: 2249–2251.

    Article  Google Scholar 

  • Yao Ping, Cao Jie, Zhang Wancheng, 2007. Interannual change of air temperature in August in longitudinal range-gorge region and its influence factors. Journal of Natural Disasters, 16(3): 49–55. (in Chinese)

    Google Scholar 

  • Yao Yonghui, Zhang Baiping, Han Fang et al., 2010. Diversity and geographical pattern of altitudinal belts in the Hengduan Mountains in China. Journal of Mountain Science, 7(2): 123–132.

    Article  Google Scholar 

  • Yelena O-H, Hamil P, Rahul R, 2009. Concrete evidence & geographically weighted regression: A regional analysis of wealth and the land cover in Massachusetts. Applied Geography, 29: 478–487.

    Article  Google Scholar 

  • Yen Shih-Min, Chiou Chyi-Rong, Chang Kang-Tsung, 2008. Modeling the species distribution of three dominant coniferous species in Taiwan. Taiwan Journal for Science, 23(2): 165–181.

    Google Scholar 

  • Zhang Yiguang, 1998. Several issues concerning vertical climate of the Hengduan Mountains. Resources Science, 20(3): 12–19. (in Chinese)

    Google Scholar 

  • Zheng Yuanchang, Gao Shenghuai, 1984. Trial discussion on the vertical natural zone of the mountains in west Sichuan. Mountain Research, 2(4): 237–244. (in Chinese)

    Google Scholar 

  • Zheng Yuanchang, Gao Shenghuai, Chai Zongxing, 1986. A preliminary study on the vertical natural zones in the Hengduan Mountains region. Mountain Research, 4(1): 75–83. (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Baiping Zhang.

Additional information

Foundation: National Natural Science Foundation of China, No.41030528; No.41001278

Author: Yao Yonghui (1975–), Ph.D, specialized in GIS/RS application and mountain environment.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yao, Y., Zhang, B. MODIS-based air temperature estimation in the southeastern Tibetan Plateau and neighboring areas. J. Geogr. Sci. 22, 152–166 (2012). https://doi.org/10.1007/s11442-012-0918-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-012-0918-1

Keywords

Navigation