Landsat Time Series for Drought Monitoring: Application Using Google Earth Engine

In recent years, this the Plain of Mekerra (Wilaya of Sidi Bel Abbes) region has been marked by signs of drought such as lack of rainfall and water stress in agricultural areas leading to shortage of agricultural crops. This requires scientific study in a systematic framework through the use of remote sensing indices to assess agricultural drought, including land surface temperature (LST), normalized difference vegetation index (NDVI), temperature condition index (TCI) and vegetation condition index. (VCI) and vegetation health index (VHI). The integrated approach utilizing Google Earth Engine (GEE) cloud computing for automate crop drought analysis showcased notable correlation between the NDVI and key indicators. Specifically, NDVI-LST displayed a negative correlation of -0.73, NDVI-TCI showed a correlation of 0.73, while NDVI-VCI exhibited a strong correlation of 0.99, and NDVI-VHI demonstrated a correlation of 0.98. these significant correlations provide valuable insights into the relationships between NDVI and various agricultural indicators for drought monitoring and assessment.