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The occurrence of drought is a complex process and is caused by the interaction of multiple drought-causing factors. The construction of traditional drought models and indexes seldom considers multiple drought-causing factors. This study integrated the precipitation, soil water and heat balance, and crop growth during drought. From the beginning of the process of agricultural drought, the atmosphere, soil, and crops that characterize drought are considered, through the principal component analysis method to construct a comprehensive drought monitoring index (CDMI). This index was verified by using the areas covered by drought, areas affected by drought, relative soil moisture, and crop yield. The annual average CDMI had negative correlations with areas covered and affected by drought. The correlation coefficients were -0.68 and -0.73. Moreover, the CDMI value had positive correlations with relative soil moisture and crop yield. The maximum correlation coefficient between CDMI and relative soil moisture was 0.91, and the correlation coefficient with maize yield was 0.52. Subsequently, the CDMI was applied to long-term drought monitoring in agricultural areas during the summer maize growing season (June to September) in Henan Province. Results showed that the most severe years of agricultural drought in Henan Province were 2000, 2001, 2004, 2006, 2008, and 2014. The most severe agricultural drought occurred in July and August 2014. Statistics found that Henan Province had high frequencies of severe drought. This study proved that CDMI calculated by multisource remote sensing data is a reliable and effective indicator for monitoring and assessing agricultural drought. |
