## 4 Discussion
### Limitations and Future Prospects  
Although this study presents a novel integration of NDVI trends with different types of drought impacts, it has some limitations that need to be addressed in future research. Firstly, the study only covers the period from 2000 to 2018, which may not fully capture long-term drought trends and vegetation adaptation cycles, especially under accelerating climate change. Secondly, while remote sensing (RS) provides broad-scale insights, coarse-resolution data (e.g., MODIS NDVI) may overlook fine-scale vegetation dynamics and localized drought impacts, particularly in heterogeneous landscapes like cropland-grassland transitions [62]. Thirdly, although croplands were identified as highly vulnerable, the study does not differentiate between crop types (e.g., maize vs. sorghum) or farming practices (e.g., irrigation vs. rainfed), which critically shape drought responses. Fourthly, the analysis does not explicitly incorporate anthropogenic influences (e.g., irrigation infrastructure, land-use changes), which may confound natural vegetation-drought relationships in agricultural zones. Finally, the Mann-Kendall test, as a widely used non-parametric trend detection method, offers advantages such as not requiring data normality and being insensitive to outliers. However, it still presents the following limitations or biases in practical applications: (1) the test assumes data point independence, but real-world time series (e.g., temperature, hydrological data) often exhibit autocorrelation, which may lead to incorrect conclusions about trend existence; and (2) the original test does not account for seasonal fluctuations (e.g., monthly temperature variations). Thus, seasonal patterns may obscure or exaggerate genuine trends.  
To deepen the interdisciplinary impact, the following aspects could be added in future studies: (1) integrating multi-sensor data (e.g., Sentinel-2, LiDAR) to enhance spatial resolution; (2) combining RS with field experiments to validate drought-vegetation interactions; (3) incorporating crop models (e.g., DSSAT) to assess management-specific adaptations; (4) applying machine learning to disentangle climate and human drivers of vegetation changes; (5) tracking vegetation responses to cyclical droughts under compounding pressures (e.g., heatwaves, land-use changes); (6) integrating economic thresholds (e.g., farmer adaptation costs) and policy scenarios (e.g., water allocation rules); (7) testing whether the basin's drought adaptation lessons apply to analogous regions (e.g., the Sahel or the Australian Murray-Darling Basin). Thus, future research should prioritize longitudinal studies tracking multi-decadal vegetation responses to increasingly frequent drought cycles [63] and mechanistic studies combining remote sensing with ground-based physiological measurements to unravel underlying processes [3]. Furthermore, to provide optimized policy recommendations, spatial resilience patterns could be translated into prioritized conservation zones (e.g., protecting drought-buffering shrublands). Early warning systems using vegetation sensitivity thresholds should also be developed [64]. Additionally, local water replenishment strategies could be aligned with transboundary water governance frameworks. Finally, socioeconomic factors and land-use changes should be integrated to develop holistic adaptation strategies.