Natural Hazards

, Volume 80, Issue 2, pp 1135–1152 | Cite as

Drought monitoring using an Integrated Drought Condition Index (IDCI) derived from multi-sensor remote sensing data

  • Lingkui Meng
  • Ting Dong
  • Wen Zhang
Original Paper


Drought is a complex natural phenomenon. To effectively characterize the spatial extent and intensity of the phenomenon, multiple drought-related factors, such as precipitation, vegetation growth condition, and land surface temperature, should be considered comprehensively. However, the capability of each of these factors in drought monitoring varies with seasonal time. Thus, in formulating a drought index, different weights should be assigned to these factors at different time periods. This study proposes a novel remote sensing index, the Integrated Drought Condition Index (IDCI), for short-term drought monitoring. The index sets different weights for each month of the growing season into three components, i.e., precipitation, vegetation growth condition, and land surface temperature, based on the principle component analysis. To assess IDCI performance, the spatial drought conditions of the IDCI maps during the growing season in a typical dry year and individual month of August from 2003 to 2012 were compared with in situ drought indices in Northern China. Correlation analyses were performed between the IDCI and different timescale Standardized Precipitation Evapotranspiration Index values, and the year-to-year IDCI variations were compared with in situ drought indices. The results of the comparison and correlation analysis confirmed the effectiveness of IDCI in characterizing drought conditions and patterns.


Drought MODIS TRMM precipitation Principle component analysis SPEI Drought index Drought index weighting 



This work was funded by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2011BAH12B06-02) and the Scientific Research in the Public Interest of the Ministry of Water Resources of China (No. 201001046). The authors are thankful to the China Meteorological Data Sharing Service System for the provision of meteorological data. Thanks to Land Processes Distributed Active Archive Center for providing the MODIS satellite images and the NASA Data and Information Services Center for providing the TRMM satellite images. Moreover, the authors express gratitude to Beguerı´a and Vicente-Serrano for providing the SPEI R Package, which is available at We are grateful to the anonymous reviewers for their valuable comments and suggestions.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina

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