Seasonally Decoupled Recovery of Flooded Cropland After the 2022 Sindh Floods: A Matched-Counterfactual Satellite Assessment

Flooded cropland in Sindh had not returned to its counterfactual vegetation condition four years after the 2022 floods, and the shortfall sits almost entirely in the summer (Kharif) season. We built 555,309 300-m cropland cells across 22 districts and tracked monthly Sentinel-2 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from January 2021 to September 2026. Cells with three or more Sentinel-1 water detections during August to October 2022 were treated as flooded and matched one-to-one with non-flooded cells on terrain, drainage proximity, cropland fraction, and pre-flood vegetation level and trend, giving 87,447 pairs with all seven covariates balanced (absolute standardized mean difference below 0.03). An event-study difference-in-differences model shows no pre-flood divergence (k = −1 coefficient +0.003, p = 0.43). Flooded cells fell 0.20 NDVI below controls in Kharif 2022, recovered to −0.06 by Rabi (winter) 2023–24, settled at about −0.11 through Kharif 2026. The result survives a placebo shock, three calipers, leave-one-district-out estimation, and a monotonic dose-response across flood-duration terciles (−0.13, −0.19, −0.28). Integrated Kharif NDVI in flooded cells stayed 0.30 to 0.60 units below controls in every summer from 2023 to 2026, whereas integrated Rabi NDVI exceeded controls from the second winter onward (flood duration × Kharif interaction, γ = −0.32, p = 4.4 × 10−10). Flood duration and post-flood standing-water persistence were the strongest predictors of time to recovery across mixed-effects, spatial, and gradient-boosted models. Trajectory clustering places 17% of matched cells in a persistent-deficit archetype; Larkana and Dadu hold 36% of them.