基于Modis地表温度的横断山区气温估算及其时空规律分析

doi:10.11821/xb201107005

摘要/Abstract

摘要： 横断山区气象观测站稀少且多分布在河谷之中,气温资料极度匮乏,严重影响山区地理与生态研究。随着遥感技术的发展,热红外遥感数据,结合地面观测数据,可以用来推测山区气温。本文通过对横断山区2001 年-2007 年间64 个气象台站的多年月平均气温数据(Ta) 与Modis地表温度多年月平均值(Ts) 进行了时序分析和回归分析,并取得如下研究结果：(1) Ts 与Ta 具有非常好的线性相关关系,89%的台站的决定系数高于0.5；95%的台站的标准误差都低于3 oC,84.4%的台站标准误差低于2.5 oC；12 个月份的Ts 与Ta 的决定系数R²在0.63~0.90 之间,标准误差在2.22~3.05 oC之间。(2) 研究区内月均气温的变化范围在-2.25~15.64 oC之间；生长季(5-9 月份) 的月均气温变化范围为：10.44~15.64 oC。(3) 等温线的海拔高度自山体外围向内部逐渐升高,与山体效应的增温效应相吻合；0 oC等温线自10 月份从海拔4700±500 m左右逐渐降低,至1月份降至最低点,约在3500±500 m左右,此后,逐渐回升,至次年5 月份再次达到4700±500 m左右,也就是说横断山区5200 m以下的广大山区全年至少有6~12 个月的气温在0 oC以上。研究表明：可以利用Modis月均地表温度结合地面观测台站的数据较精确的估算山区月均气温。

关键词: 气温估算, 地表温度, Modis, 时序分析, 横断山区

Abstract: Climatic conditions are difficult to obtain in high mountain areas due to few meteorological stations and, if any, their poorly representative locations in valleys. Fortunately, remote sensing data can 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 Hengduan Mountains. A total of 64 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 (mean R² = 0.72) and the mean standard error of 2.07 oC. The regression analysis of monthly mean Ts vs. Ta for every month of all the stations shows that monthly mean Ts can be used to accurately estimate monthly mean Ta (R² ranging from 0.63 to 0.90 and standard error between 2.22 oC and 3.05 oC). Thirdly, the retrieved monthly mean Ta for the whole study region varies between -2.25 oC (in January, the coldest month) and 15.64 oC (in July, the warmest month), and for the warm (growing) season (May-September), it is from 10.44 oC to 15.64 oC. Finally, the elevation of isotherms is greater in the central mountain ranges than that in the outer margins; the 0 oC isotherm occurs at elevations of about 4700±500 m in October, and it drops to 3500±500 m in January, and ascends back to 4700±500 m in May next year, which means that monthly mean Ta in the areas below 5200 m is above 0 oC for 6 to 12 months. This clearly indicates that MODIS data could be used to have an accurate estimation of air temperature in mountain regions.

Key words: land surface temperature, MODIS, air temperature estimation, spatial-temporal analysis, the Hengduan Mountains

姚永慧, 张百平, 韩芳. 基于Modis地表温度的横断山区气温估算及其时空规律分析[J]. 地理学报, 2011, 66(7): 917-927.

YAO Yonghui, ZHANG Baiping, HAN Fang. MODIS-based Air Temperature Estimation in the Hengduan Mountains and Its Spatio-temporal Analysis[J]. Acta Geographica Sinica, 2011, 66(7): 917-927.

参考文献

[1] Barry R G. MountainWeather and Climate. London and New York: Routledge, 1992.

[2] Barry R G. MountainWeather and Climate. Boulder, USA: University of Colorado, 2008.

[3] Flenley J. Ultraviolet insulation and the tropical rainforest: Altitudinal variations, Quaternary and recent change,extinctions, and biodiversity//Tropical Rainforest Responses to Climatic Change. Springer, 2007: 219-235.

[4] Holtmeier F.-K. Mountain timberlines: Ecology, patchiness, and dynamics//Advances in Global Change Research. Boston:Kluwer Academic Publishers, 2003.

[5] Wang Yuenan, Zhang Bo, Chen Longxun et al. Relationship between the atmospheric heat source over Tibetan Plateau andthe heat source and general circulation over East Asia. Chinese Science Bulletin, 2008, 53(21): 3387-3394.

[6] Fang Jingyun. Study on the geographic elements affecting temperature distribution in China. Acta Ecologica Sinica, 1992,12(2): 97-104. [方精云. 地理要素对我国温度分布影响的数量评价. 生态学报, 1992, 12(2): 97-104.]

[7] Han Fang, Zhang Baiping, Tan Jin et al. The effect of mountain base elevation on the altitude of timberline in thesoutheastern Eurasia: A study on the quantification of mass elevation effect. Acta Geographica Sinica, 2010, 65(7):781-788. [韩芳, 张百平,谭靖等. 山体基面高度对欧亚大陆东南部林线分布的影响: 山体效应定量化研究. 地理学报,2010, 65(7): 781-788.]

[8] Smith W L, Leslie L M, Diak G R et al. The integration of meteorological satellite imagery and numerical dynamicalforecast models. Philosophical Transactions Royal Society of London, 1988, 324: 317-323.

[9] IPCC, Climate change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge, United Kingdom: Cambridge University Press, Retrieved fromtheWorldWideWeb: http://www.grida.no/publications/other/ipcc_tar/, 2001: 881.

[10] Connor S J, Thomson M C, Flasse S P et al. Environmental information systems in malaria risk mapping and epidemicforecasting. Disasters, 1998, 22(1): 39-56.

[11] Focks D A, Daniels E, Haile D G et al. A simulation-model of the epidemiology of urban dengue fever: Literatureanalysis, model development, preliminary validation, and samples of simulation results. American Journal of TropicalMedicine and Hygiene, 1995, 53(5): 489-506.

[12] Goetz S J, Prince S D, Small J. Advances in satellite remote sensing of environmental variables for epidemiologicalapplications. Advances in Parasitology, 2000, 47: 289-307.

[13] Carlson T N, Buffum M J. On estimating total daily evapotranspiration from remote surface temperature measurements.Remote Sensing of Environment, 1989, 29(2): 197-207.

[14] Guerschman J P, Van Dijk A I J M. Scaling of potential evapotranspiration with MODIS data reproduces flux observationsand catchment water balance observations across Australia. Journal of Hydrology, 2009, 369(1/2): 107-119.

[15] Liu R, Chen J M, Liu J et al. Application of a new leaf area index algorithm to China's landmass using MODIS data forcarbon cycle research. Journal of Environmental Management, 2007, 85(3): 649-658.

[16] Sandholt I, Andersen H S. Derivation of actual evapotranspiration in the Senegalese Sahel, using NOAA-AVHRR dataduring the 1987 growing season. Remote Sensing of Environment, 1993, 46(2): 164-172.

[17] Santanello Jr J A, Peters-Lidard C D, Garcia M E et al. Using remotely-sensed estimates of soil moisture to infer soiltexture and hydraulic properties across a semi-arid watershed. Remote Sensing of Environment, 2007, 110(1): 79-97.

[18] Shu Y, Stisen S, Jensen K H et al. Estimation of regional evapotranspiration over the North China Plain usinggeostationary satellite data. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(2): 192-206.

[19] De Wit A J W, Van Diepen C A. Crop growth modeling and crop yield forecasting using satellite-derived meteorologicalinputs. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(4): 414-425.

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

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

[22] Vancutsem C, Ceccato P, Dinku T et al. Evaluation of MODIS land surface temperature data to estimate air temperature indifferent ecosystems over Africa. Remote Sensing of Environment, 2009, 114: 449-465.

[23] Cresswell M P, Morse A P, Thomson M C et al. Estimating surface air temperatures, from Meteosat land surfacetemperatures, using an empirical solar zenith angle model. International Journal of Remote Sensing, 1999, 20(6):1125-1132.

[24] Mostovoy G V, King R L, Reddy K R et al. Statistical estimation of daily maximum and minimum air temperatures fromMODIS LST data over the state of Mississippi. GIScience and Remote Sensing, 2006, 43(1): 78-110.

[25] Jones P, Jedlovec G, Suggs R et al. Using MODIS LST to estimate minimum air temperatures at night//The 13thConference on Satellite Meteorology and Oceanography, Norfolk, VA: AMS 4.13, 2004, From theWorldWideWeb: http://ams.confex.com/ams/pdfpapers/79017.pdf.

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

[27] Prihodko L, Goward S N. Estimation of air temperature from remotely sensed surface observations. Remote Sensing ofEnvironment, 1997, 60(3): 335-346.

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

[29] Hao Zhenchun, Jiang Weijuan, Ju Qin et al. The features of climate changes in the five river source regions of the TibetanPlateau. Journal of Glaciology and Geocryology, 2010, 32(6): 1130-1136. [郝振纯, 江微娟, 鞠琴等. 青藏高原河源区气候变化特征分析. 冰川冻土, 2010, 32(6): 1130-1136.]

[30] He Yunling, Zhang Yiping. The climate characteristics and change trends on basins of Lancangjiang Valley in YunnanProvince. Journal of Mountain Science, 2004, 22(5): 539-548. [何云玲, 张一平. 澜沧江干流河谷盆地气候特征及变化趋势. 山地学报, 2004, 22(5): 539-548.]

[31] Li Zongxing, He Yuanqing, Xin Huijuan et al. Spatio-temporal variations of temperature and precipitation in Mts.Hengduan Region during 1960-2008. Acta Geographica Sinica, 2010, 65(5): 563-579. [李宗省, 何元庆, 辛惠娟等. 我国横断山区1960-2008 年气温和降水时空变化特征. 地理学报, 2010,65(5): 563-579.]

[32] Wan Yunxia, Zhang Wancheng, Xiao Ziniu. Spatiotemporal variation characteristics of air temperature in LongitudinalRidge-gorge Region of Yunnan in recent century. Journal of Natural Disasters, 2009, 18(5): 183-189. [万云霞, 张万诚,肖子牛. 近百年云南纵向岭谷区气温的时空变化特征. 自然灾害学报, 2009, 18(5): 183-189.]

[33] Yao Ping, Cao Jie, Zhang Wancheng. Interannual change of air temperature in August in longitudinal range-gorge regionand its influence factors. Journal of Natural Disasters, 2007, 16(3): 49-55. [姚平, 曹杰, 张万诚. 纵向岭谷区8 月气温的年际变化及其影响因子.自然灾害学报, 2007, 16(3): 49-55.]

[34] Meng Xiangfei, Wei Hong, Xie Xiaohong et al. Study of the spatial distribution of land surface temperature of YunnanProvince based on MODIS data. Journal of Southwest University: Natural Science Edition, 2010, 32(12): 154-158. [孟翔飞, 魏虹, 谢小红等. 基于MODIS 数据的云南地表温度空间分布特征. 西南大学学报: 自然科学版, 2010,32(12):154-158.]

[35] Zhang Yiguang. Several issues concerning vertical climate of the Hengduan Mountains. Resources Science, 1998, 20(3):12-19. [张谊光. 横断山区垂直气候的几个问题. 资源科学, 1998, 20(3): 12-19.]

[36] Zheng Yuanchang, Gao Shenghuai. Trial discussion on the vertical natural zone of the mountains in west Sichuan.Mountain Research, 1984, 2(4): 237-244. [ 郑远昌, 高生淮. 略论川西山地的自然垂直带. 山地研究, 1984, 2(4):237-244.]

[37] Zheng Yuanchang, Gao Shenghuai, Chai Zongxing. A preliminary study on the vertical natural zones in the HengduanMountains region. Mountain Research, 1986. 4(1): 75-83. [郑远昌, 高生淮, 柴宗新. 试论横断地区自然垂直带. 山地研究, 1986, 4(1): 75-83.]

[38] Quervain A.d. Die Hebung der atmosphärischen lsothermenin der Schweizer Alpen und ihre Beziehung zu derenHöhengrenzen. Gerlands Beitr. Geophys., 1904, 6: 481-533.

[39] Yen S-M, Chiou C-R, Chang K-T. Modeling the species distribution of three dominant coniferous species in Taiwan.Taiwan Journal for Science, 2008, 23(2): 165-181.

[40] Troll C. The upper timberlines in different climatic zones. Arctic and Alpine Research, 1973, 5(3): 3-18.

[41] Miehe G, Miehe S, Vogel J et al. Highest treeline in the Northern Hemisphere found in southern Tibet. Mountain Researchand Development, 2007, 27(2): 169-173.