Journal of Integrative Agriculture ›› 2013, Vol. 12 ›› Issue (1): 159-168.DOI: 10.1016/S2095-3119(13)60216-6

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DetectingAgro-Droughts in Southwest of China Using MODIS Satellite Data

 ZHANG Feng, ZHANG Li-wen, WANG Xiu-zhen , HUNG Jing-feng   

  1. 1.Institute of Agricultural Remote Sensing & Information Application, Zhejiang University, Hangzhou 310058, P.R.China
    2.The Provinical Key Laboratories of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou 310058, P.R.China
    3.Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education/College of Natural Resources and Environmental Science, Zhejiang University, Hangzhou 310058, P.R.China
    4.Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, P.R.China
  • 收稿日期:2012-06-18 出版日期:2013-01-01 发布日期:2013-01-24
  • 通讯作者: Correspondence HUANG Jin-feng, Tel: +86-571-88982830, Fax: +86-571-63370364, E-mail:erajps@163.com
  • 作者简介:ZHANG Feng, Mobile: 15088677853, E-mail: feng_zhang@zju.edu.cn
  • 基金资助:

    This research was supported by the National Key Technologies R&D Program of China (2011BAD32B01) and the Ph D Programs Foundation of Ministry of Education of China (20100101110035).

DetectingAgro-Droughts in Southwest of China Using MODIS Satellite Data

 ZHANG Feng, ZHANG Li-wen, WANG Xiu-zhen , HUNG Jing-feng   

  1. 1.Institute of Agricultural Remote Sensing & Information Application, Zhejiang University, Hangzhou 310058, P.R.China
    2.The Provinical Key Laboratories of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou 310058, P.R.China
    3.Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education/College of Natural Resources and Environmental Science, Zhejiang University, Hangzhou 310058, P.R.China
    4.Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, P.R.China
  • Received:2012-06-18 Online:2013-01-01 Published:2013-01-24
  • Contact: Correspondence HUANG Jin-feng, Tel: +86-571-88982830, Fax: +86-571-63370364, E-mail:erajps@163.com
  • About author:ZHANG Feng, Mobile: 15088677853, E-mail: feng_zhang@zju.edu.cn
  • Supported by:

    This research was supported by the National Key Technologies R&D Program of China (2011BAD32B01) and the Ph D Programs Foundation of Ministry of Education of China (20100101110035).

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摘要： The normalized difference vegetation index (NDVI) has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag between rainfall deficit and NDVI response. To better understand this relationship, time series NDVI (2000-2010) during the growing season in Sichuan Province and Chongqing City were analyzed. The vegetation condition index (VCI) was used to construct a new drought index, time-integrated vegetation condition index (TIVCI), and was then compared with meteorological drought indices-standardized precipitation index (SPI), a multiple-time scale meteorological-drought index based on precipitation, to examine the sensitivity on droughts. Our research findings indicate the followings: (1) farmland NDVI sensitivity to precipitation in study area has a time lag of 16-24 d, and it maximally responds to the temperature with a lag of about 16 d. (2) We applied the approach to Sichuan Province and Chongqing City for extreme drought monitoring in 2006 and 2003, and the results show that the monitoring results from TIVCI are closer to the published China agricultural statistical data than VCI. Compared to VCI, the best results from TIVCI3 were found with the relative errors of -4.5 and 6.36% in 2006 for drought affected area and drought disaster area respectively, and 5.11 and -5.95% in 2003. (3) Compared to VCI, TIVCI has better correlation with the SPI, which indicates the lag and cumulative effects of precipitation on vegetation. Our finding proved that TIVCI is an effective indicator of drought detection when the time lag effects between NDVI and climate factors are taken into consideration.

关键词: time-integrated vegetation condition index (TIVCI) , time lag , normalized difference vegetation index (NDVI) , drought monitor , standardized precipitation index (SPI)

Abstract: The normalized difference vegetation index (NDVI) has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag between rainfall deficit and NDVI response. To better understand this relationship, time series NDVI (2000-2010) during the growing season in Sichuan Province and Chongqing City were analyzed. The vegetation condition index (VCI) was used to construct a new drought index, time-integrated vegetation condition index (TIVCI), and was then compared with meteorological drought indices-standardized precipitation index (SPI), a multiple-time scale meteorological-drought index based on precipitation, to examine the sensitivity on droughts. Our research findings indicate the followings: (1) farmland NDVI sensitivity to precipitation in study area has a time lag of 16-24 d, and it maximally responds to the temperature with a lag of about 16 d. (2) We applied the approach to Sichuan Province and Chongqing City for extreme drought monitoring in 2006 and 2003, and the results show that the monitoring results from TIVCI are closer to the published China agricultural statistical data than VCI. Compared to VCI, the best results from TIVCI3 were found with the relative errors of -4.5 and 6.36% in 2006 for drought affected area and drought disaster area respectively, and 5.11 and -5.95% in 2003. (3) Compared to VCI, TIVCI has better correlation with the SPI, which indicates the lag and cumulative effects of precipitation on vegetation. Our finding proved that TIVCI is an effective indicator of drought detection when the time lag effects between NDVI and climate factors are taken into consideration.

Key words: time-integrated vegetation condition index (TIVCI) , time lag , normalized difference vegetation index (NDVI) , drought monitor , standardized precipitation index (SPI)