Journal of Agricultural Big Data >
Drought Monitoring and Spatiotemporal Changes Analysis in North China Plain Based on Temperature Vegetation Dryness Index
Received date: 2023-05-15
Online published: 2023-05-16
Drought is the main reason for the imbalance between supply and demand of water resources and the shortage of water resources in the region. The occurrence of drought is a long-term, continuous and complex process, which is the result of the comprehensive action of atmosphere, soil and crops. Drought has a great impact on the production and life of the people all over the country. How to better monitor drought is of great significance to industry, agriculture and people's daily life. Vegetation index and temperature can describe the response of vegetation to drought stress, thus reflecting the soil water status. In this paper, the temperature vegetation dryness index is established by using the two-dimensional characteristic space of vegetation index and surface temperature. The unique ecological and physiological significance of the two indexes is combined. The temperature vegetation dryness index from 2010 to 2019 (10 years) is selected to monitor and analyze the drought in the North China Plain. Drought is divided into five grades (heavy humidity, light humidity, normal, light drought, heavy drought), from the annual change seasonal and monthly changes analyze the temporal and spatial changes of drought. The results show that the drought is mainly mild drought, and the drought situation shows an upward trend. From the perspective of season, drought often occurs in summer. From the month, the highest and lowest temperature vegetation dryness index appeared in July and January respectively. The study of drought in North China Plain provides a reliable research support for drought resistance and drought prevention in this region.
ZHANG Zhaoxu, CUI Jin, GOU Wentao, XIAO Yue . Drought Monitoring and Spatiotemporal Changes Analysis in North China Plain Based on Temperature Vegetation Dryness Index[J]. Journal of Agricultural Big Data, 2023 , 5(1) : 95 -107 . DOI: 10.19788/j.issn.2096-6369.230118
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