Spatial-temporal patterns of agricultural drought severity in the Lake Urmia Basin Iran: A cloud-based integration of multi-temporal and multi-sensor remote sensing data

The Lake Urmia Basin (LUB) in Iran has been experiencing significant drought conditions with severe impacts on its agricultural sector. This study analyzed the spatiotemporal patterns of agricultural drought across the LUB during growing seasons from 2000 to 2019. The analysis integrated multiple drought indices including the Vegetation Condition Index (VCI), Thermal Condition Index (TCI), Vegetation Health Index (VHI), Composite Index (CI), Precipitation Condition Index (PCI), and Rainfall Anomaly Index (RAI). These indices were derived from 8,279 satellite images combining MODIS, TRMM, and GridSat-B1 data processed in Google Earth Engine. The study's primary innovation lies in developing a real-time algorithm for mapping spatial patterns and calculating temporal trends of agricultural drought through time-series analysis of drought indices in a cloud-based environment. For validation purposes, Standardized Precipitation Index (SPI) values at 3-, 6-, and 9-month intervals were calculated using precipitation data from seven synoptic stations. Positive Pearson correlations were observed between VCI and all SPI intervals across stations, with most stations showing significant correlations (R=0.35). VCI analysis revealed that while most of the LUB remained unaffected by drought, the remaining areas experienced mild, moderate, severe, and extreme drought conditions in descending order of extent. The maximum and minimum extents of extreme and severe drought were observed during 2000-2018, while moderate and mild drought peaks occurred during 2000-2002 and 2002-2018, respectively. Analysis of Variance (ANOVA) with Duncan Post Hoc testing demonstrated significant differences among indices, though VHI and CI showed no significant distinction from each other. The PERSIANN-CDR-derived RAI identified five major drought events between 2000 and 2019, showing a correlation of R=0.576 with the mean RAI from the seven stations. Temporal trend analysis using Ordinary Least Squares (OLS) regression revealed that 73.3% and 20% of the LUB experienced wet and drying trends, respectively, with 22.2% showing significant wet trends and 1.7% showing significant drying trends.