Land quality degradation exacerbates the impact of drought on vegetation in Northeast China

• Forest exhibits a higher dependence on land quality compared to crop and grass. • Vegetation demonstrates a trade-off between resistance and recovery when facing different disturbances. • Land quality degradation exacerbates drought effects on vegetation. Climate change has intensified the global water cycle, leading to more extensive, concurrent, and frequent occurrences of drought. Water serves as a critical driver of soil carbon dynamics, which in turn significantly influences soil quality. However, the coupled effects of drought and land degradation on vegetation resilience remain poorly understood, particularly in ecologically vulnerable and strategically important regions like Northeast China’s black soil zone. Here, we conducted a comprehensive regional assessment to quantify the vulnerability, resistance, and recovery of three major vegetation types (forest, crop, and grassland) under varying drought intensities and land degradation levels in Northeast China from 2001 to 2020. Our approach leveraged multi-source remote sensing data and meteorological reanalysis to derive the Standardized Precipitation Evapotranspiration Index (SPEI), a Land Quality Index (LQI), and Solar-Induced Chlorophyll Fluorescence (SIF) as a proxy for vegetation productivity. Drought events and degradation states were identified based on their natural occurrence and statistical thresholds within the study period. Our findings reveal that land degradation markedly amplifies drought impacts, increasing vegetation vulnerability and reducing resistance. Notably, forests, while displaying the highest resistance, were the most dependent on land quality and became the most vulnerable under coupled stresses. We also observed a trade-off between resistance and recovery across ecosystems. These results provide mechanistic insights into vegetation dynamics under coupled stresses and highlight the necessity of incorporating land quality feedbacks into vegetation models for improved predictive capability under climate change.