Please wait a minute...
img img
高级检索
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     
基于植被指数和土地表面温度的干旱监测模型
王鹏新1,2,Wan Zhengming3,龚健雅4,李小文1,2,王锦地1,2
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
ADVANCES IN DROUGHT MONITORING BY USING REMOTELY SENSED NORMALIZED DIFFERENCE VEGETATION INDEX AND LAND SURFACE TEMPERATURE PRODUCTS
Wang Pengxin1,2,Wan Zhengming3,Gong Jianya4,Li Xiaowen1,2,Wang Jindi1,2
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
 全文: PDF(135 KB)  
摘要:

干旱是一种周期性发生的自然现象,其发生过程中有关参数如地表覆盖度、温度和土壤表层含水量等可以通过遥感的途径进行定量反演,而这些参数客观地反映了地表的综合特征。综述了运用遥感反演产品---土地表面温度和归一化植被指数在干旱监测中的应用前景和进展,分析了距平植被指数、条件植被指数、条件温度指数和归一化温度指数等干旱监测方法的优缺点,在前人研究的基础上,提出了条件植被温度指数的干旱监测模型,探讨了其应用前景。

关键词: 干旱监测遥感土地表面温度归一化植被指数    
Abstract:

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.

Key words: Drought monitoring    Remote sensing    Land surface temperature    Normalized difference vegetation index    Vegetation temperature condition index.
收稿日期: 2002-07-12 出版日期: 2003-08-01
:  Q15  
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.

通讯作者: 王鹏新     E-mail: pengxinwang@263.net
作者简介: 王鹏新(1965-),男,陕西省礼泉县人,博士后,主要从事遥感技术在农业中的应用研究.E-mail:pengxinwang@263.net
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
WANZhengming
王锦地
李小文
龚健雅
王鹏新

引用本文:

王鹏新,Wan Zhengming,龚健雅,李小文,王锦地. 基于植被指数和土地表面温度的干旱监测模型[J]. 地球科学进展, 2003, 18(4): 527-533.

Wang Pengxin,Wan Zhengming,Gong Jianya,Li Xiaowen,Wang Jindi. ADVANCES IN DROUGHT MONITORING BY USING REMOTELY SENSED NORMALIZED DIFFERENCE VEGETATION INDEX AND LAND SURFACE TEMPERATURE PRODUCTS. Advances in Earth Science, 2003, 18(4): 527-533.

链接本文:

http://www.adearth.ac.cn/CN/10.11867/j.issn.1001-8166.2003.04.0527        http://www.adearth.ac.cn/CN/Y2003/V18/I4/527

[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.

[1] 马晋, 周纪, 刘绍民, 王钰佳. 卫星遥感地表温度的真实性检验研究进展[J]. 地球科学进展, 2017, 32(6): 615-629.
[2] 晋锐, 李新, 马明国, 葛咏, 刘绍民, 肖青, 闻建光, 赵凯, 辛晓平, 冉有华, 柳钦火, 张仁华. 陆地定量遥感产品的真实性检验关键技术与试验验证[J]. 地球科学进展, 2017, 32(6): 630-642.
[3] 李青, 雷连发, 王振会, 魏鸣, 李东帅. 雷电流热效应的遥感观测研究进展[J]. 地球科学进展, 2017, 32(5): 481-487.
[4] 李正泉, 宋丽莉, 马浩, 冯涛, 王阔. 海上风能资源观测与评估研究进展[J]. 地球科学进展, 2016, 31(8): 800-810.
[5] 彭志兴, 周纪, 李明松. 基于地面观测的异质性下垫面像元尺度地表温度模拟研究进展[J]. 地球科学进展, 2016, 31(5): 471-480.
[6] 张 勇, 戎志国, 闵 敏. 中国遥感卫星辐射校正场热红外通道在轨场地辐射定标方法精度评估[J]. 地球科学进展, 2016, 31(2): 171-179.
[7] 崔月菊, 杜建国, 李营, 刘雷, 周晓成, 陈扬, 陈志, 韩晓昆. 张渤地震带高光谱气体地球化学特征[J]. 地球科学进展, 2016, 31(1): 59-65.
[8] 吴炳方, 邢强. 遥感的科学推动作用与重点应用领域[J]. 地球科学进展, 2015, 30(7): 751-762.
[9] 兰鑫宇, 郭子祺, 田野, 雷霞, 王婕. 土壤湿度遥感估算同化研究综述[J]. 地球科学进展, 2015, 30(6): 668-679.
[10] 吴珊珊, 姚治君, 姜丽光, 刘兆飞. 现代冰川体积变化研究方法综述[J]. 地球科学进展, 2015, 30(2): 237-246.
[11] 黄磊, 李震, 周建民, 田帮森. SAR监测冰川变化研究进展[J]. 地球科学进展, 2014, 29(9): 985-994.
[12] 尹剑, 占车生, 顾洪亮, 王飞宇. 基于水文模型的蒸散发数据同化实验研究[J]. 地球科学进展, 2014, 29(9): 1075-1084.
[13] 权凌, 周纪, 李明松, 代冯楠, 李国全. 基于时间序列建模的城市热岛时间尺度成分分离方法与应用[J]. 地球科学进展, 2014, 29(6): 723-733.
[14] 袁文平, 蔡文文, 刘丹, 董文杰. 陆地生态系统植被生产力遥感模型研究进展[J]. 地球科学进展, 2014, 29(5): 541-550.
[15] 陈洪萍, 贾根锁, 冯锦明, 董燕生. 气候模式中关键陆面植被参量遥感估算的研究进展[J]. 地球科学进展, 2014, 29(1): 56-67.