Evaluating the Efficiency of Remote Sensing Based Indicators (VCI, TCI, VHI) in Order to Monitor the Level of Drought in the South of Kerman Province

Drought is one of the natural phenomena caused by the decrease in rainfall and reduces vegetation and increases dust. The common method of drought monitoring is based on observational data from meteorological stations and calculates a drought index. Knowing the amount and intensity of drought in a region and planning to reduce its effects is one of the most important principles of management in regional planning fight against drought. Monitoring and management of drought is a region using remote sensing data and satellite images as a suitable tool in the temporal and spatial monitoring of agricultural drought has always been  of interest to regional managers.

This study investigates the efficacy of remote sensing data and satellite images in delineating agricultural drought zones in the southern watershed of Kerman Province from 2001 to 2022. For the purpose, three indices, including the Vegetation Cover Index(VCI),Temperature Condition Index (TCI), and Vegetation Health Index (VHI), were extract from MODIS satellite images, and TerraClimate extracts data for growing season months based on remote sensing of vegetation cover from early March to late August (2001-2022), and the resulting indicators are compared with standard precipitation index (SPI) values. It was compared in one, three, six, nine-, twelve-, twenty-four-, and forty-eight-month time series. In this study, the correlation method between stations was employed to reconstruct the incomplete station data, and the stations with the highest correlation with the incomplete station were utilized for reconstruction. After organizing the data, the reconstructed information was entered into the SPIGenerator software as input, and the SPI indices for 1, 3, 6, 9, 12, 24, and 48 months were generated for the selected stations.

In this article, the results of calculating the SPI index in various time series based on the average data from existing stations and remote sensing data were analyzed. To this end, the values obtained from all satellite-based indices, including VCI, VHI, and TCI, were extracted and compared with the ground-based SPI index in 1, 3, 6, 9, 12, 24, and 48- month time series. In other words, the response of vegetation cover in the region to rainfall with 1, 3, 6, 9, 12, 24, and 48- month time legs was investigated. The result of this study shows that the VCI index has a significant correlation at a 1% level with the SPI index in all time series except for the 1-month series. The TCI index has a significant correlation with none of the time series except for the 1-month series. The VHI index has a significant correlation at a 1% level with all time series except for the 1-month,3-month, and 24-month series. Additionally, the drought indices TCI, VHI, and VCI have a positive significant correlation at a 1% level with rainfall and soil moisture.

The overall results of the analysis of the available data showed that among the studies indices, the VCI index has the highest correlation with SPI values in different time series during the growing season, with a significant correlation at the 0.01 level. Therefore, it is selected as the preferred satellite index for monitoring agricultural drought in southern Kerman Province. The results of this study provide a basis for a better understating and evaluation of drought conditions in southern Kerman Province, and can guide optimized management and provision of drinking water and agricultural resources, leading to increased and optimized agricultural production, and ultimately, proper environmental protection.