Evaluation of meteorological drought indices using remote sensing

Drought indices are a suitable tool for management measures and dealing with drought and are widely used worldwide. One of the most important stages of assessing the drought situation in each region is determining the drought indices to analyze the intensity and duration of drought in that region. The drought index is actually a function of various environmental factors that affect the drought phenomenon. In this study, the drought indices of the standardized precipitation index (SPI), China-Z index (CZI), Z Score Index (ZSI), and modified CZI (MCZI) in different month time scales (1, 3, 6, 9, 12, 18, 24 and 48) and the present of normal index (PNI) in different monthly, seasonally and yearly time scales in Kurdistan Province (stations Saqqez, Qorveh, Bijar, Sanandaj) were evaluated. Global land data assimilation system (GLDAS), Climatic Research Unit (CRU) dataset, and Tropical Rainfall Measuring Mission (TRMM) precipitation data (2000–2020) and TRMM precipitation (2000–2019) were received, and the drought indices were calculated. Root mean square error (RMSE), maximum error (ME), Pearson, and Spearman correlation coefficients were used for evaluation. The results of the SPI index showed that there is a significant relationship between TRMM, CRU, and GLDAS at the Saqqez, Qorveh, and Sanandaj stations (at the 5% level), and there was no significant relationship for TRMM at Bijar station for the 24-month time scale. The correlation coefficient results for the Saqqez and Sanandaj stations in time scales of 1–9 months for the SPI, CZI, ZSI, and MCZI indices were better than those of the Bijar and Qorveh stations. In assessing the SPI and CZI drought indices, the highest RMSE was for GLDAS at Bijar station and for the 48-month time scale. In general, the results showed that the drought indices of the SPI, CZI, ZSI, MCZI, and PNI obtained from the TRMM satellite, CRU dataset, and GLDAS model have a good correlation with the drought indices of synoptic stations. • Using different meteorological drought indices such as the SPI, CZI, ZSI, PNI, and MCZI. • Using remote sensing data such as TRMM, CRU and GLDAS in drought indices. • Studying of drought indices in different remote sensing data.