A Spatiotemporal Drought Analysis Application Implemented in the Google Earth Engine and Applied to Iran as a Case Study

Abstract

Drought is a major problem in the world and has become more severe in recent decades, especially in arid and semi-arid regions. In this study, a Google Earth Engine (GEE) app has been implemented to monitor spatiotemporal drought conditions over different climatic regions. The app allows every user to perform analysis over a region and for a period of their choice, benefiting from the huge GEE dataset of free and open data as well as from its fast cloud-based computation. The app implements the scaled drought condition index (SDCI), which is a combination of three indices: the vegetation condition index (VCI), temperature condition index (TCI), and precipitation condition index (PCI), derived or calculated from satellite imagery data through the Google Earth Engine platform. The De Martonne climate classification index has been used to derive the climate region; within each region the indices have been computed separately. The test case area is over Iran, which shows a territory with high climate variability, where drought has been explored for a period of 11 years (from 2010 to 2021) allowing us to cover a reasonable time series with the data available in the Google Earth Engine. The developed tool allowed the singling-out of drought events over each climate, offering both the spatial and temporal representation of the phenomenon and confirming results found in local and global reports.

Keywords

• Google Earth Engine
• drought
• remote sensing
• De Martonne aridity index
• scaled drought condition index