Elevated groundwater-induced crop waterlogging stress (EGCWS) threatens groundwater-sensitive agroecosystems (GWSA), yet its concealed nature makes monitoring challenging. This study developed an integrated monitoring framework for China's Gahai Irrigation District. The framework combined multi-source data and a random forest algorithm to spatially downscale root-zone soil moisture, from which a Soil Moisture Condition Index (SMCI) was constructed to identify EGCWS events during 2018-2022. Vegetation responses to EGCWS were quantified using Spearman's correlation and random forest variable importance. The results revealed a clear phased mitigation of EGCWS. The area affected by high-frequency events (>5 occurrences) dropped significantly from 7.71%-33% (2018-2019) to below 0.04% (2021-2022), with 2020 as the turning point. EGCWS primarily suppressed vegetation growth within the GWSA, as evidenced by negative SMCI-NDVI correlations (rho < 0) across 77.81-99.86% of the area. Notably, the ecological implication of this negative correlation shifted from 'waterlogging inhibition' during 2018-2019 to 'recovery response' during 2021-2022. Cumulative effects suppressed vegetation more strongly and rapidly than lag effects, increasing the proportion of negative impacts by 16% and negative responses within 0-200 days by 26.86%. This study provides important methodological guidance and risk management insights for other arid and semi-arid agricultural regions worldwide facing similar groundwater-induced waterlogging stress.