In semi-arid regions, the production of rain-fed agricultural activity is a majorly risky operation because sensitivity is very high to climate extremes, including drought and other calamities (Choi et al. 2013; He et al. 2021). Several researchers have noticed that drought events cause a serious decline in agricultural productivity and production all over the globe. This can happen with no caution, without identified economic or borders and political differences (KOGAN 1990). For example, during the time of periods 2001–2012, extreme-exceptional (EE) covered about 1–7% of Severe-exceptional (SE) 8–16%, moderate to-exceptional (ME) 18–36%, of the total land area of the globe, respectively. Respectively(Kogan et al. 2013), For instance, the droughts period in Russia from 2010 and 2011 to 12 in the United States of America produced substantial global and local economic impacts (Kogan and Guo 2016). As an outcome, the balance of food demand and supply was affected significantly due to extreme and severe droughts (SD) at global, regional, and local scales level (Hoolst et al. 2016). In semi-dry regions, where the precipitation pattern is extremely variable, the susceptible collapse is realized (Maybank et al. 1995). Different regions of the globe, mainly the grain-growing nations like the USA, China, Russia, India, and the European Union are thus encountered an incline in the intensity and frequency of droughts events (Owrangi et al. 2011) .In developed nations, drought mitigation, monitoring, and early warning structure are situated on earth observation data products and it is most effective, while in most Asian countries (including India) the location depends highly on the in-situ climatic data format only, Which largely affects the smallholder farmers of the countries. It also scarcity the continuous temporal and spatial range needed to monitor and characterize the detailed temporal pattern and spatial extent of drought events (Gu et al. 2007). Karnataka is one of the main revenue states of India; some regions of the state were affected by frequent drought periods and events due to erratic and poor precipitation variability where the problem is extreme and severe in the south-eastern parts of the state. Some researchers reported that the occurrence of El Nino climate event droughts and dry events has also been regularly occurring over the several decades triggering different threats to the agriculture sector. Particularly the semi-arid area has been majorly affected by the recurrent droughts events (Harishnaika et al. 2022). The duration, cessation, severity, frequency, and spatial extent of drought in the regions are high. Despite the substantial growth and health in the major crop types maize, wheat, barley, sorghum, and other crops). were noticed in terms of area and productivity coverage, these yields are low when assess by international standards. Because production is largely susceptible to weather events, particularly dry events and drought. Agricultural cultivation and production, majorly in the poor regions have endured highly dependent on the climate and weather (A. Zhang et al. n.d.). The challenges stand up may also in the future as the natural resources are highly overexploited due to increasing population growth. Agriculture is the sector firstly affected by the hydro-meteorological period droughts because it negatively affects vegetation growth as well as crop production (Bhuiyan et al. 2006), but behind move on to other water resource-dependent sectors (Komuscu 2001). Agricultural drought is expressed by the depletion of crop productivity and production due to a shortfall of precipitation as well as insufficient soil moisture to the zone of crop root (Sruthi and Aslam 2015). However, the dependency on weather and climate data alone is not enough to monitor and prediction in the region of drought events, especially when these data are sparse, untimely, and incomplete (Peters et al. 2002). The conventional ways of dry events monitoring which highly depend only on weather grid stations lack repeated spatial coverage to monitor and characterize the spatial pattern of dry incidences in-depth (Gu et al. 2007). Monitoring the health of vegetation status of the research area is significant to describe the events of agricultural drought, then it requires 5 years of satellite data observation suitable drought indices Furthermore, the monitoring, mitigating, and understanding of drought are become a difficult aspect because of the natural phenomenon (Vicente-serrano et al. 2012). Yet, satellite data observations have some limitations to meteorological observations, giving the possibility for cost-effective, spatially and temporarily dynamic and explicit scale drought monitoring (Zhang et al. 2016). Satellite product observation like NDVI, eMODIS, and MOD11A2 LST supported with highly advanced remote sensing drought indexes such as VHI (Vegetation Health Index) can help to evaluate the occurrence of agricultural droughts events. Kogan and Liu (1996) express that the seasonal and inter-annual drought events can be represented by using the VCI (Vegetation Condition Index) and TCI-Temperature Condition Index (L. Zhang et al. 2019) because both indexes can help to calculate and generate VHI (Rhee et al. 2010). Vegetation Health Index has been the accepted agricultural drought indices. but, it needs both LST and NDVI data (Gidey et al. 2018). The target of this research was to monitor the agricultural drought for 5 years period of duration, onset, cessation, severity, frequency, and spatial and temporal extent utilizing the Vegetation Health Index (VHI) which combines NDVI, LST, VCI, and TCI in Kolar and Chikkaballapura district area of Karnataka state. The study is conclusive for understanding, monitoring, and managing the events of droughts through meteorological and satellite earth observation data.