Measuring drought agriculture by using ADSI and VHI indices and determining crops irrigation effects (case study: Markazi province).

Drought Agricultural is the most repeatedly natural disaster, particularly in arid/semi-arid regions. It causes diversity losses such as crop quality, production, utility rate, and food security as the most important threat. In this study, first, SPEI index is calculated whereby precipitation and temperature data, respectively. The correlation analysis performed between crop yield anomalies and SPEI index to determine critical month/months in which drought severity is severe, and crop reproductive stage is vulnerable. After this step, the best timescale of SPEI is demonstrated and the slope between the best SPEI and crop yield anomalies is calculated to compute ADSI values. Finally, for appreciation of irrigation effects in response to drought stress, analysis of covariance and panel data regression due to SPEI values and crop yield are applied. In addition, the relationship between the SPEI, VHI, and ADSI will be discussed. It is necessary to state that the majority of crops in the region of interest are studied. Also, for obtaining VHI, LST, and NDVI indices as MODIS products have been utilized in the study area. For the most crops, The results indicated that July was a critical month and for the residual crops, August was a critical month with severe drought, whereas some crops had three critical months. Furthermore, significant amount of the crops have been damaged in the flowering stage, plus the corn was more sensitive to drought relative to other crops. The results derived from panel data model regression show that drought sensitivity is incremental in non-irrigation harvest and vice versa. In other words, non-irrigated crops are more sensitive to drought and drought stress increase in an arid and semi-arid acres, significantly. At the end, comparing results of VHI and ADSI imply their strong relationship. ADSI and VHI were able to quantitatively measure the drought intensity. Also, the panel data regression was able to discriminate irrigation and non-irrigation effects. [ABSTRACT FROM AUTHOR]