Study Effects of agricultural drought on vegetation density using remote sensingCase study: Simineh Rood Watershed Basin

In recent years, lower than the water level of Lake Urmia, the study of changes in temperature, humidity and drought stress thresholds in the area of inevitability is inevitable.Drought is a difficult weather event and a "crawl" disaster. From the agricultural perspective, drought has occurred when the soil moisture is less than the actual needs of the product and leads to damage in the product. For drought analysis, the existence of an indicator for the accurate and reliable determination of wet and dry periods is very necessary.

In this descriptive-analytical and purpose-oriented study. The MODIS sensor data based on the Terra satellite was used. In these images, 8-day composite products with a resolution of 1 km and 8-day surface reflectance with a resolution of 250 m at time interval (2005 to 2008) were used for five consecutive months from June to October. Linear regression was used to derive the results. Also in this study 5.1ENVI, 10.5ARC GIS was used for analysis.The purpose of this study is to use MODIS sensor to evaluate the spatial relationship between NDVI-Ts and NDVI-ΔT for extracting real-time agricultural droughts in the Simineh River watershed from the catchment area of Lake Urmia, The VTCI Index (Vegetable Temperature Index) and WDI (Water Deficiency Indicator), which are capable of identifying regional drought stress. The second component of the material used in this research are:1. MODIS/Terra Land Surface Temperature/Emissivity8-Day L3Global 1km SIN Grid V004MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V004 .2MODIS products produce surface temperature and reflectivity, their reflection and pixel magnitude. The VTCI index (duration of drought) is based on a simplified NDVI-TS triangular space in which the "cold edge" (non-stressed conditions) is considered as a line that has the lowest temperature in the NDVI axis (X-axis) and the "hot edge" (non-availability of water) is interpreted as a negative link with the NDVI. The WDI index (drought severity) can be further labeled as the wet line or ETR with a maximum of ΔLST NDVI min and the dry line or ETR is at least equal to Δ LST NDVI max.

The vegetation temperature index (VTCI) is calculated based on the relationship between the NDVI-TS triangular space. The hot and cold edge pixels are derived as linear regression equations for calculating VTCI using ENVI software in mathematical data, in which ground temperature (LST) and NDVI index images are used as input parameters for the VTCI equations Is In all these years, it is observed that the gradient for the hot edge is negative, while for the cold edge it is slightly negative or no appreciable change. The gradient shows that when the NDVI value increases for any time interval, the maximum temperature decreases. The gradient on the cold edge indicates that when the NDVI value increases.The Water Deficit Index (WDI) is calculated on the basis of the ΔTs-NDVI spatial relationship, the wet line pixels and the dry line are determined by the linear regression equation, and the extracted equations were used to calculate WDI. In calculating this index, the difference between the surface Temperature (TS) extracted from the satellite imagery and the temperature of the weather stations (Ta) as a parameter (ΔT), and NDVI images are used as another input parameter for the WDI equations. Therefore, the wet and dry line is obtained from the NDVI-ΔTS triangular barrier. The slope obtained from the NDVI-ΔTS spatial relationship is negative for the dry line, while the gradient obtained for the wet line is positive and in the rest of the years there was no significant change. The negative slope shows that ΔT Max decreases with increasing NDVI index and positive slope shows that ΔT Min increases with increasing NDVI index.The study period shows that the 2005-2006 period in the study area has a higher VTCI content than the years 2007-2008, which means less stress. The low VTCI has a tendency to stress and its high value indicates favorable conditions. The low VTCI value is less than its higher value in the obtained maps, which expresses the relative favorable condition of drought stress throughout the studied region. Generally, the lower Simineh, upstream of Bookan County, and to some extent the Hajiabad Plain, Miandoab, are areas that show less moisture conditions during 2005-2007. These areas, namely the Simineh River (between Miandoab and Bookan), show lower VTCI than the Simineh Rivers (between Mahabad and Bookan) and Simineh River higher (between Bookan and Saggez). The more northerly regions with high VTCI levels are less humid and have good conditions. VTCI is higher in these areas due to irrigation options that vegetation does not tend to moisture.The WDI spatial pattern has also been studied in the Simineh River watershed from the main catchment area of Urmia Lake for the July 153 day in 2005-2008. The lower WDI represents the optimal conditions, while the higher VTCI represents the favorable conditions in that area, and the range of this index is the exact opposite of the VTCI index. According to the maps drawn from the WDI index, it can be seen that during the period from 2006 to 2008, the WDI spatial pattern is similar to the VTCI index. Although the spatial patterns of both indicators are similar, the WDI index offers a distinct class of this situation. In the entire catchment area of the Simineh River, the water stresses were severe in 2005, but gradually increased from 2006 to 2008. In the middle and upper part of the upper part, the WDI is high, indicating that all areas between Bookan and Mahabad are exposed to moisture stress. The northern Bookan districts of the northwest and the Haji-Abad area of Miandoab show relatively good conditions, and in 2008, all mid-zone regions such as the eastern, western and northern Akhtachi show a higher WDI.The overall analysis shows that the duration and severity of stress are similar according to the VTCI index as well as the WDI index from favorable conditions to stress conditions, and shows that in general, in 2005-2007, the temperature and humidity stresses in the study area gradually, the tensions peaked in 2008.