Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia

Precise assessment of drought and its impact on the natural ecosystem is an arduous task in regions with limited climatic observations due to sparsely distributed situ stations, especially hyper-arid region Kingdom Saudi Arabia (KSA). Therefore, this study investigates application remote sensing techniques monitor compare sensing-retrieved indices (RSDIs) standardized meteorological index (Standardized Precipitation Evapotranspiration Index, SPEI) during 2001–2020. The computed RSDIs include Vegetation Condition Index (VCI), Temperature (TCI), Health (VHI), which are derived using multi-temporal Landsat 7 ETM+, 8 OLI/TIRS satellites, Google Earth Engine (GEE) platform. Pearson correlation coefficient (CC) used find extent agreement between SPEI RSDIs. comparison showed CC values 0.74, 0.67, 0.57, 0.47 observed for VHI/SPEI-12, VHI/SPEI-6, VHI/SPEI-3, VHI/SPEI-1, respectively. Comparatively low was TCI 0.60, 0.61, 0.42, 0.37 TCI/SPEI-12, TCI/SPEI-6, TCI/SPEI-3, TCI/SPEI-1. A lower 0.53, 0.45, 0.33 0.24 VCI/SPEI-12, VCI/SPEI-6, VCI/SPEI-3, VCI/SPEI-1, Overall, results suggest that VHI better correlated suitable monitoring data-scarce regions. This research will help improve our understanding relationships indices.