Evaluating the Effectiveness of Phase Difference in Early Drought Detection

This research investigates how different phase relationships can enhance our understanding of drought effects on moisture deficiency in desert ecosystems—a significant and damaging environmental issue impacting natural ecosystems, economies, health, agriculture, and society. The primary objective is to examine the variance in lag times between fixed and dynamic lag windows correlated with the NDVI (Normalized Difference Vegetation Index), aiming to develop an optimal methodology for drought analysis in the Thar Desert.Utilizing remote sensing data, the study explores the complex drought dynamics of the Thar Desert by analyzing 22 years of CHIRPS rainfall time series data and MODIS NDVI product. The research involves cross-correlating rainfall with NDVI, comparing lag time differences between fixed lag windows (16, 32, 48, 64 days) and dynamic lag windows (ranging from 4 to 64 days with incremental steps) against 22 years of MODIS NDVI data.Preliminary results indicate that dynamic lag windows of 4, 8, 12, 16, and 64 days exhibit the highest correlation with NDVI, with a lag time of 40 days showing the maximum correlation. These findings suggest that dynamic lag windows more effectively capture the temporal variability of drought impacts on vegetation compared to fixed lag windows in the Thar Desert. Further analysis with a sub-dynamic lag window, incorporating the highly correlated lag episodes of both dynamic and fixed windows (i.e., 40 daysand 48 days), revealed that a lag phase of 42 days provides the highest correlation with vegetation.Additionally, the study identifies a significant drought event in 2002, highlighting the sensitivity of the dynamic lag approach in detecting extreme drought occurrences. This research not only advances drought analysis methodologies for arid regions but also underscores the need for future studies to explore the applicability of dynamic lag windows in diverse regions and assess their predictive capacity for forecasting drought-induced vegetation changes.