MONITORING OF AGRICULTURAL DROUGHT USING FORTNIGHTLY VARIATION OF VEGETATION CONDITION INDEX (VCI) FOR THE STATE OF TAMIL NADU, INDIA
India being an agrarian nation widely depends upon rainfall for its agricultural productivity. The failure of rainfall and hence shortfall of productivity badly affects national economy. With an intricate nature of drought, the planning and management requires rigid monitoring for better understanding. The occurrence of drought and its severity varies in a regional level. The process of monitoring agricultural drought in a regional level requires long term analysis of vegetation. In this present work, the attempt has been carried out to study and monitor the spatial and temporal variation of agricultural drought for the state of Tamilnadu, India which is more prone to drought especially due to monsoon failure or change in monsoon. The long term Normalized differenced vegetation index (NDVI) of Global Inventory Modelling and Mapping Studies (GIMMS) for the period of 20 years (1984–2003) was used to compute the most popular index called vegetation condition index (VCI) to identify the vegetation vigour. The fortnightly variation of VCI during major crop growing period of Kharif season (June to September) was used to monitor the spatio-temporal drought conditions of Tamil Nadu. The results proved that there is wide variation of drought intensity among the districts within the state. The keen observation of fortnightly variation of long term agricultural drought helps finding the onset, period and spatial extent of drought in various districts of the state. The districts which are most often prone to moderate to severe drought conditions during the analysis period were recognized in order to develop various strategies to improve the agricultural productivity in that region. The persistent drought in the state necessitates the government to take appropriate preventive measures to evade drought in future. Based on the severity of the drought level observed from the agricultural drought intensity maps prepared using VCI, the action plans could be prioritized by identifying the high risk zones.