A remote sensing-based method for drought monitoring using the similarity between drought eigenvectors

The land surface temperature (LST) and vegetation growth status are two direct indicators of drought. In this study, we selected the LST index and vegetation index to construct drought eigenvectors, then proposed a new remote sensing drought index to assess the drought severity by calculating the si...

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Bibliographic Details
Published in:International journal of remote sensing 2019-12, Vol.40 (23), p.8838-8856
Main Authors: Song, Chao, Yue, Cuiying, Zhang, Wen, Zhang, Dongying, Hong, Zhiming, Meng, Lingkui
Format: Article
Language:English
Subjects:
Coefficients
Correlation analysis
Correlation coefficient
Correlation coefficients
Drought
Drought index
Drought monitoring
Eigenvectors
Environmental monitoring
Land cover
Land surface temperature
Remote monitoring
Remote sensing
Similarity
Soil
Soil moisture
soil water
Surface temperature
Vegetation
Vegetation growth
Vegetation index
Water stress
Water temperature
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Summary:The land surface temperature (LST) and vegetation growth status are two direct indicators of drought. In this study, we selected the LST index and vegetation index to construct drought eigenvectors, then proposed a new remote sensing drought index to assess the drought severity by calculating the similarity between the drought eigenvector of the target pixel and the drought eigenvector under an extremely wet state. Considering the different responses of various objects to drought, the drought eigenvectors of different land cover types were established. The results showed that the Temperature-Vegetation Water Stress Index (T-VWSI) were highly correlated with the measured relative soil moisture (RSM). The correlation coefficients (r) between the T-VWSI and 20-cm RSM reached 0.81, 0.77, and 0.78 in May, June, and July, respectively. Therefore, the T-VWSI is a promising drought index that will play an important role in drought monitoring.
ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2019.1624860