Validation of Remote Sensing Data From Google Earth Engine Using Observed Meteorological Data for Spatiotemporal Drought Hazard Assessment

ABSTRACT Precise and timely drought monitoring is increasingly critical as global warming intensifies the frequency and severity of droughts, yet historical assessments are limited by data scarcity. While remote sensing (RS) provides useful coverage, its spatial and temporal constraints reduce local accuracy, highlighting the need for improved methods to assess drought intensity and spatiotemporal dynamics in data‐scarce regions. This study analyses the level and spatiotemporal characteristics of meteorological drought in the eastern part of the Amhara Region using remote RS data in the Google Earth Engine (GEE) platform, and validated against observed meteorological data. The agreement between RS and observed rainfall data was evaluated using the coefficient of determination ( R 2 ), mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). Meteorological drought conditions were further assessed using the Standardised Precipitation Index (SPI) at annual, Kiremt (summer), and Belg (spring) time scales. The results show a strong and statistically significant relationship between RS and observed rainfall data for all time periods of the study. The Belg season exhibited the highest performance ( R 2 = 0.95, ME = 0.32 and MAE = 4.05), indicating excellent agreement. The analysis also revealed that meteorological droughts were recurrent in the study area over the past 24 years, with seven long‐term (SPI6, SPI9, and SPI12) drought events occurring during 2002–2004, 2004–2005, 2008–2010, 2011–2012, 2013, 2014–2017, and 2022–2023. Moreover, the annual meteorological drought conditions closely followed the Kiremt season drought patterns. Given the frequent and unpredictable occurrence of meteorological droughts in the region, it is recommended that farmers adopt drought‐resistant crop varieties to minimise crop failure, enhance food security, and improve the resilience of local farming systems against future drought events.