Analysis of spatiotemporal droughts using order statistics and archetype analysis of remotely sensed relative productivity index

The study focuses on northern Tunisia, where rainfed cereal crops are predominantly cultivated. This area is particularly vulnerable to droughts, which significantly impact agricultural productivity. Drought data on cereal crop damage were obtained from the National Reports (JORT) over 21 years (2000/01–2020/21). This study proposes to identify droughts using the productivity index (KV), a satellite-derived ratio of actual to potential evapotranspiration based on MODIS data, which reflects climate, soil, and vegetation conditions. Three drought identification methods were evaluated: (1) order statistics (M1a: 25th percentile of the minimum; M1b: minimum of the median; M1c: 25th percentile of the median); (2) a four-class classification based on percentiles (M2: severe, moderate, mild humid, and humid); and (3) archetype analysis (M3), which identifies extreme states on the convex hull of the data. The results demonstrate the effectiveness of KV in drought detection. Method M1a and M1b missed one drought year (2019–20), while M1c produced a false detection. Method M2 correctly identified the four most severe droughts and classified four additional years as moderate droughts, aligning with JORT reports. Archetype analysis (M3) revealed that the three archetypes best distinguished drought conditions, with declared drought years showing the smallest weights (<0.045) relative to the favorable crop archetype (A1). A four-archetype model introduced minor errors (one false alarm and one undetected drought). Notably, the weights associated with favorable (A1) and unfavorable (A2, A3) archetypes correlated strongly with reported crop damage percentages. These findings highlight the robustness of satellite-derived productivity indices and archetype analysis for large-scale drought monitoring in semi-arid regions like northern Tunisia. • Identifying droughts using the remote sensing productivity index ( KV ). • Drought data on cereal crop damage were obtained from the National Reports ( JORT ) over 2000/01–2020/21. • Three drought identification methods are considered based on order statistics and archetype analysis. • Results show robust estimation and archetypes effectively detect drought years using satellite productivity indices.