Characterization of Drought Detection With Remote Sensing Based Multiple Indices and SPEI in Northeastern Ethiopian Highland

Abstract

Drought detection is crucial for managing risk, often using continuous drought indicators derived from satellite data, rainfall, and other hydrometeorological variables. The study examined meteorological and agricultural drought patterns in North Wollo, Ethiopia, using various indices such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Standardized Precipitation-Evapotranspiration Index (SPEI), and Vegetation Drought Index (VDI) from 2000 to 2022. The study utilized satellite-derived data like MOD11A2 LST Terra and MOD13Q1 NDVI, as well as ground-based data like rainfall. The study employs multiple linear regression correlation analysis to examine the correlation between indices and climate variables such as precipitation, air temperature, and soil moisture. The findings indicated that the average LST was high (37.11°C–42.83°C), whereas the NDVI was low and unhealthy (<0.33) in the lowland area. It revealed that the lowland region had higher mean LST and lower NDVI values due to less favorable moisture conditions compared to central and highland regions. The regression analysis result revealed a significant strong negative correlation with NDVI and LST ( R = -.977, p < .01) across all study districts. This study also identified the existence of positive relationships between VHI and rainfall ( R 2 = .996, p < .01), VHI and SSM ( R 2 = .956, p < .01), and NDVI and SSM ( R 2 / p = .97/.01) in all study regions. The study found a positive linear correlation between VDI and VHI with ( R 2 = .74, p < .01) across the study region, despite negative correlations between NDVI-LST and NDWI-LST. Both VHI and Soil Moisture (SSM) indices serve as valuable indicators for monitoring the development of both meteorological and agricultural droughts in this study area. The study aids in drought monitoring in Northeastern Ethiopian Highland by identifying the most effective drought indices for assessing meteorological and agricultural drought.