地球科学进展 ›› 2003, Vol. 18 ›› Issue (4): 527 -533. doi: 10.11867/j.issn.1001-8166.2003.04.0527

研究论文 上一篇    下一篇

王鹏新 1,2,Wan Zhengming 3,龚健雅 4,李小文 1,2,王锦地 1,2   
  1. 1.北京师范大学遥感与GIS研究中心,资源与环境科学系;2.环境遥感与数字城市北京市重点实验室,北京 100875;3.Institute for Computational Earth System Science,University of California,Santa Barbara,CA 93106,USA ;4. 武汉大学测绘遥感信息工程国家重点实验室,湖北 武汉 430079
  • 收稿日期:2002-07-12 修回日期:2003-02-28 出版日期:2003-12-20
  • 通讯作者: 王鹏新 E-mail:pengxinwang@263.net
  • 基金资助:

    国家重点基础研究发展规划项目“地球表面时空多变要素的定量遥感理论与应用”(编号:G2000077900);US NASA项目(ContractNAS5-31370)资助.


Wang Pengxin 1,2,Wan Zhengming 3,Gong Jianya 4,Li Xiaowen 1,2,Wang Jindi 1,2   

  1. 1. Research Center for Remote Sensing and Department of Geography,Beijing Normal University;2. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities,Beijing  100875,China;3. Institute for Computational Earth System Science,University of California,Santa Barbara,CA  93106,USA;4. National Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan  430079,China
  • Received:2002-07-12 Revised:2003-02-28 Online:2003-12-20 Published:2003-08-01


Drought is a slow-onset natural disaster, an insidious, creeping phenomenon;it occurs in virtually all climatic regimes. Land surface parameters, such as land cover, land surface temperature and soil surface moisture can be retrieved by using remote sensing techniques during the period of drought occurrence. The advances and prospects of applying remotely sensed normalized difference vegetation index(NDVI) and land surface temperature(LST) products for monitoring drought are reviewed. The advantages and disadvantages of NDVI and LST based drought monitoring models are analyzed. Anomaly vegetation index(AVI), vegetation condition index(VCIW) and temperature condition index(TCI) are NDVI or LST based drought monitoring approaches, and study NDVI or LST differences between NDVI or LST values at a specific period of a year and their multi-year's averages or maximum and minimum values at the specific period. It is promising to develop drought monitoring approaches which integrate NDVI and LST products. These approaches are based on the correlation between NDVI and LST and have more physical interpretations than those of using NDVI or LST products alone. The ratio of LST and NDVI is a simple approach, the disadvantage of this approach is that it is difficult to obtain quantitative indices for describing drought intensity. We develop a drought monitoring approach called vegetation temperature condition index (VTCI), and verify that VTCI is a near-real time drought monitoring approach. VTCI is not only related to NDVI changes, but also related to land surface temperature changes. It is defined as the ratio of LST differences among pixels with the same NDVI value in a sufficiently large study area, the numerator is the difference between maximum LST of the pixels and LST of one pixel, the denominator is the difference between maximum and minimum LST of the pixels. VTCI is lower for drought and higher for wet conditions.


[1] Steiner J L, Day J C, Rapendick R I. Improving and sustaining productivity in dryland regions of developing countries[J]. Advances in Soil Science,1988,8:79-118.

[2] Li Shengxiu, Xiao Ling. Distribution and management of dryland in the People’s Republic of China[J]. Advances in Soil Science,1992,18:148-278.

[3] Wilhite D A, Glantz M H. Understanding the drought phenomenon: The role of definitions[J]. Water International, 1985,10:111-120.

[4] Lambin E F, Ehrlich D. The surface temperature-vegetation index for land cover and land cover change analysis[J]. International Journal of Remote Sensing,1996,17:463-487.

[5] Liu W, Kogan F N. Monitoring regional drought using the vegetation condition index[J]. International Journal of Remote Sensing,1996,17:2 761-2 782.

[6] Palmer W C. Meteorological Drought Research Paper No. 45[R].Washington DC: Weather Bureau,1965. 1-58

[7] Mckee T B, Doeskin N J, Kleist J. The relationship of drought frequency and duration to time scales[A]. Proceeding of 8th Conference on Applied Climatology[C]. January 17-23, 1993, American Meteorological Society, Boston, Massachusetts, 1993.179-184.

[8] Mckee T B, Doeskin N J, Kleist J. Drought monitoring with multiple time scales[A]. Proceeding of 9th Conference on Applied Climatology[C]. January 15-20, American Meteorological Society, Boston, Massachusetts, 1995. 233-236.

[9] Guttman, N B. Accepting the standardized precipitation index: A calculation algorithm[J]. Journal of the American Water Resources Association,1999,35:311-323.

[10] Wang Pengxin, Wei Yimin. Research, Demonstration and Extension of Sustainable Farming Systems for Rainfed Agriculture(UN -DP-CPR /91/114 Project Final Report)[M]. Xi’an: World Publishing Corporation, 1998.

[11] Li Xiaowen,Gao Feng,Wang Jindi,et al. A priori knowledge accumulation and its application to linear BRDF model inversion[J]. Journal of Geophysical Research(D11),2001,106:11 925-11 935.

[12] Li Xiaowen, Wang Jindi, Hu Baoxin, et al. A priori knowledge in remote sensing reversion[J]. Science in China(D),1998,28:67-72.[李小文,王锦地,胡宝新,.先验知识在遥感反演中的作用[J].中国科学:D,1998,28:67-72.]

[13] Dawson M S, Fung A K, Marry M T. A robust statistical-based estimator for soil moisture retrieval from radar measurement[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35:57-67.

[14] Jackson R D, Slater P N, Pinter P J. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres[J]. Remote Sensing of Environment,1983,13:187-208.

[15] Moran M S, Clarke T R, Inoue Y, et al. Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index[J]. Remote Sensing of Environment,1994,49:246-263.

[16] Chen Wenying, Xiao Qianguang, Sheng Yongwei. Application of the anomaly vegetation index to monitoring heavy drought in 1992[J]. China Remote Sensing of Environment,1994,9:106-112.[陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].环境遥感,1994,9:106-112.]

[17] Kogan F N. Remote sensing of weather impacts on vegetation in non-homogeneous areas[J]. International Journal of Remote Sensing,1990,11:1 405-1 419.

[18] McVicar T R, Jupp D L B. The current and potential operational use of remote sensing to aid decisions on drought exceptional circumstances in Australia: A review[J]. Agricultural System,1998,57:399-468.

[19] Liu W T, Ferreira A. Monitoring crop production regions in the Sao Paulo State of Brazil using normalized diference vegetation index[A]. Proceeding of the 24th International Symposium on Remote Sensing of Environment [C]. Chicago: ERIM,1991,2:447-455.

[20] Di L,Rundquist D C, Han L. Modeling relationship between NDVI and precipitation during vegetative growth cycles[J]. International Journal of Remote Sensing,1994,15:2 121-2 136.

[21] Kogan F N. Application of vegetation index and brightness temperature for drought detection[J]. Advances in Space Research,1995,15:91-100.

[22] McVicar T R, Jupp D L B, Yang X, et al. Linking regional water balance models with remote sensing[A]. In: Proceedings of the 13th Asian Conference on Remote Sensing[C]. Ulaanbaatar, Mongolia:1992. B6.1-B6.6.

[23] Goetz S J. Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site[J]. International Journal of Remote Sensing,1997,18:71-94.

[24] Idso B, Schmugge T, Jackson R, et al. The utility of surface temperature measurements for remote sensing of soil moisture[J]. Journal of Geophysical Research,1975,80:3 044-3 049.

[25] Carlson T N, Gillies R R, Perry E M. A method to make use of thermal infrared temperature and NDVI management to infer surface soil water content and fractional vegetation cover[J]. Remote Sensing Reviews, 1994, 9:161-173.

[26] Gillies R R, Carlson T N, Cui J, et al. A verification of the tri-angle’ method for obtaining surface soil water content and energy fluxes from remote measurement of the Normalized Difference Vegetation Index(NDVI) and surface radiant temperature[J]. International Journal of Remote Sensing,1997,18:3 145-3 166.

[27] Price J C. Using spatial context in satellite data to infer regional scale evapotranspiration [J]. IEEE Transactions on Geoscience and Remote Sensing,1990,28:940-948.

[28] Gillies R R, Carlson T N. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporating into climate models[J]. Journal of Applied Meteorology,1995,34:745-756.

[29] Nemani R, Running S W. Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation[J]. Agriculture and Forest Meteorology,1989,44:245-260.

[30] McVicar T R, Bierwirth P N. Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery[J]. International Journal of Remote Sensing,2001,22:2 109-2 128.

[31] Teng W L. AVHRR monitoring of U.S. crops during the 1988 drought[J]. Photogrametric Engineering and Remote Sensing,1990,56:1 143-1 146.

[32] Lozano-Garcia D F, Fernandez R N, Gallo K P, et al. Monitoring the1988 severe drought in Indiana, USA using AVHRR data[J]. International Journal of Remote Sensing,1995,16:1 327-1 340.

[33] Wang Pengxin, Gong Jianya, Li Xiaowen. Vegetation temperature condition index and its application for drought monitoring[J]. Geometics and Information Sciences of Wuhan University,2001,26:412-418.[王鹏新,龚健雅,李小文.条件植被温度指数及其在干旱监测中的应用[J].武汉大学学报:信息科学版,2001,26:412-418.]

[34] Wang Pengxin, Wan Zhengming, Li Xiaowen. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA[A]. In: Sobrino J A, ed. The Recent Advances in Quantitative Remote Sensing[C]. Valencia:Publicacions de la Universitat de Valencia, Spain, 2002, 664-671.

[35] Cracknell A P, Xue Y. Thermal inertia determination from space: A tutorial review[J]. International Journal of Remote Sensing, 1996, 17:431-461.

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