Application of ESACCI SM product-assimilated to a statistical model to assess the drought propagation for different Agro-Climatic zones of India using copula

•The statistical model derived total column SM was highly correlated to ERA5 SM.•The drought propagation is identified and analysed by copula.•Gumbel copula dominates as the best fit copula between SPI and SSI.•The monsoon has highest propagation time, followed by summer, spring, and winter. Meteorological drought precedes the agricultural drought and studying the propagation time from meteorological to agricultural drought can substantially reduce agricultural losses. To find this propagation time between the meteorological and agricultural drought, this study analyzed the copula-based conditional probability between the Standardized Precipitation Index at 12 timescales (SPI-1 to12, meteorological drought) and Standardized Soil Moisture Index at 1 month timescale (SSI-1, agricultural drought), over the fifteen Agro-Climatic Zones (ACZs) of India. SSI is computed using a total column Soil Moisture (SM) derived from ESACCI SM using the Statistical Soil Moisture Profile (SSMP) model. The SSMP-based ESACCI SM is positively correlated (Correlation Coefficient of 0.871) with ERA5 Land SM. To compute the conditional probability, three copulas namely Frank, Clayton, and Gumbel copulas are fitted between SPI-1 to 12 and SSI-1. The Goodness of Fit analysis showed the dominance of the Gumbel copula among the Clayton, Frank, and Gumbel copulas. Gumbel copula completely dominated the SPI timescales of 1–3 and 11–12 whereas Clayton copula was relatively favored for SPI timescales of 4–10. The propagation time results revealed that the monsoon season (JAS) consistently exhibits the highest propagation time, averaging around 10 months followed by summer (AMJ, 7 months), spring (JFM, 4 months), and winter (OND, 2–3 months) seasons. The Mann-Kendall trend test revealed that the same season can have different trends in different ACZs and vice versa, underlining the localized and season-specific strategies for drought management, considering each ACZ's unique environmental, agricultural, and socio-economic conditions in each zone. Overall, this study showcases the capability of remote sensing and copula-based statistical models, in understanding meteorological to agricultural drought propagation. It also highlights the efficiency of statistical models in deriving total column SM from surface SM.