Analysis of spatiotemporal variations of drought and its correlations with remote sensing-based indices via wavelet analysis and clustering methods

Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT–EEMD–K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satellite sensor data (i.e. the Normalized Difference Vegetation Index, the Vegetation Condition Index, the Vegetation Healthy Index, and the Temperature Condition Index) was evaluated. The possible links between central stations of clusters and satellite-based indices were assessed via the wavelet coherence method. The results revealed that all applied satellite-based indices had significant statistical correlations with the ground-based drought index within a certain period.