Abstract:
Drought is a multifaceted natural hazard that has an impact on various aspects like; agricul
tural, hydrological, ecological and socio-economic systems. Drought has been a predomi
nant concern for farmers in Gamo Zone over the last decades; hence, monitoring drought
is important for soil conservation, water planning and management to mitigate impacts
on agriculture in the Zone. The vegetation indices normal difference vegetation index and
vegetation condition index are popular since they are based only on satellite images. The
present study attempts to monitoring spatiotemporal distribution of agricultural droughts
and its association with land surface temperature, precipitation and soil moisture from the
period 2000–2020 using remote sensing application method in Gamo Zone. The vegetation
condition index (VCI) and Normal difference vegetation index (NDVI) were used to evalu
ate spatial and temporal distribution of agricultural drought in Gamo Zone. The Pearson
correlation method was used to identify Normal difference vegetation index association
with land surface temperature, soil moisture and precipitation. The results discovered that
severe drought to very severe drought was identified in 2000, 2002, 2004, 2008, 2009 and
2015 with the area coverage 39.7%, 28.8%, 33.4%, 24.5%, 61.3% and 23.0%, respectively.
Similarly, the findings show that slight to mild droughts have a great chance of occurrence
in Gamo Zone. The data also demonstrate that normal difference vegetation index has a
strong relationship with land surface temperature (R = − 0.95), precipitation (R = 0.65) and
soil moisture (R = 0.85), indicating that normal difference vegetation index is more sensi
tive to land surface temperature than soil moisture and precipitation, although soil moisture
has a positive relationship with precipitation (R = 0.60). Therefore, this study revealed that
normal difference vegetation index and vegetation condition index indices are suitable for
agricultural drought monitoring and they are strongly associated with precipitation, soil
moisture and land surface temperature. This study shall be helpful for decision makers to
take the necessary measures by considering the drought risk maps for early warning system
and future drought management plans.
