Studying correlation between rainfall and NDVI/MODIS for Time Series (2012 - 2022) in arid region in Syria

Vegetation degradation is correlated with drought. The more drought intensifies, the more degraded vegetation increases.

Therefore, this study aimed to assess the correlation between rainfall and changes in the Normalized Difference Vegetation Index (NDVI) under arid and semi-arid conditions in Syria.

The study was carried out using annual rainfall data for (2012-2022 ) obtained from the Agricultural cloud seeding Project, to determine the average rainfall of the study area and to link it to the NDVI index of MODIS image data processed using the Google Earth Engine (GEE) for April of each year for the same time series. The results showed that the lowest NDVI value (0.098) was in (2016), representing the driest year during the studied series, while the highest NDVI value (2.4) was in 2019, which coincided with the highest rainfall rate of 206.67 mm, thus representing the less arid year during the same series.

It also found a strong correlation (R=0.7) between the overall average rainfall and the overall NDVI values of the studied time series.

The NDVI maps, which were classified as( -0.2 - 0.8), using ArcGIS 10.8.2, showed that arid land with a simple herbal coverage (0-0.1) occupied 90% of the total study area with the exception of 2019, where pastures and rain-fed crops (0.3-0.4) occupied 85.45% of the total study area. The study has shown that changes in the NDVI index are associated with changes in rainfall, indicating that they can be used to estimate and study drought as a simple method derived from satellite data in isolation from ground data.