Evaluation of the visible and shortwave infrared drought index in China

Open Access
Article

DOI: 10.1007/s13753-013-0008-8

Cite this article as:
Zhang, N., Hong, Y., Qin, Q. et al. Int J Disaster Risk Sci (2013) 4: 68. doi:10.1007/s13753-013-0008-8

Abstract

In this article, the performance of the Visible and Shortwave infrared Drought Index (VSDI), a drought index recently developed and validated in Oklahoma, United States, is further explored and validated in China. The in-situ measured soil moisture from 585 weather stations across China are used as ground-truth data, and five commonly used drought indices are compared with VSDI for surface drought monitoring. The results reveal that VSDI is robust and reliable in the estimation of surface dryness-it has the highest correlation with soil moisture among the six indices when computed using both the original and cloud removed data. All six indices show the highest correlation with soil moisture at the 10 cm layer and the averaged 10–50 cm layer. The spatiotemporal patterns of surface moisture indicated by the MODIS-based VSDI are further compared with the precipitation-based drought maps and the Global Land Data Assimilation System (GLDAS) simulated surface soil moisture maps over five provinces located in the Middle-Lower Yangtze Plain of China. The results indicate that despite the difference between the spatial and temporal resolutions of the three products, the VSDI maps still show good agreement with the other two drought products through the rapidly alternating drought and flood events in 2011 in this region. Therefore, VSDI can be used as an effective surface wetness indicator at both the provincial and the national scales in China.

Keywords

China drought map drought monitoring optical remote sensing soil moisture VSDI 
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© The Author(s) 2013

This article is published under license to BioMed Central Ltd. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.Institute of Remote Sensing and GISPeking UniversityBeijingChina
  2. 2.School of Civil Engineering and Environmental SciencesUniversity of OklahomaNormanUSA
  3. 3.National Satellite Meteorological Center of China Meteorological AdministrationBeijingChina

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