Geospatial Observations Using Vegetation Temperature Condition Index for Drought Conditions Over the Cropland of Punjab, Pakistan
The stochastic characterization of drought in the plain of Punjab (Pakistan) was strongly motivated by the fact that the reliance on precipitation data is not sufficient for drought monitoring, taking into account the lack of reliable and complete data, together with proper network information systems, particularly the lower availability of weather-stations in Pakistan. The Satellite observations reveal the study site in a spatio-temporal pattern with a near-real time index called vegetation temperature condition index (VTCI) to exhibit the drought conditions using EOS’s satellite Aqua-MODIS NDVI and LST data products in response to drought and soil moisture, to reflect the agricultural and hydrological impact in the plain. The spatial and temporal observations represent mostly significant drought conditions in the northeast in contrast to centre and south with insignificant drought conditions for the winter wheat crop seasons in the year of 2004, 2006, 2008, 2010 and 2011 during 2003-2014 under both rainfed and irrigated conditions in the region. This divulges that the south and center of the plain expose to drought in contrast to the northeast with high occurrences of drought, whereas, the northeast indicates most of the normal conditions of drought in the plain. The study exhibits the temporal changes of the land surface drought conditions through the periods of 16 day to 12 month accumulative precipitation with a significant correlation of the VTCI time series values, varies with four periods (2003-2008, 2010, 2011-2014, 2003- 2014) at five weather-stations during 2003-2014. This reveals the effectiveness of multi-year MODIS VTCI approach observations for the determination of warm and cold edges, drought monitoring and flood events in the given periods over the plain of Punjab. Results exemplify VTCI as the favorable index for the dry and wet condition during the winter wheat crop seasons.
Jahangir khan and Pengxin Wang