准确预测植被动态对于理解陆地生态系统碳循环和水循环至关重要。然而，现有的归一化植被指数（NDVI）预测方法在表征时空异质性及多驱动变量间的非线性相互作用方面存在局限，限制了大尺度下的预测精度和外推性能。为解决上述问题，本研究提出了一种融合多变量约束的结构化大尺度 NDVI 时空预测方法（MCS-LSTP），以刻画复杂异质环境中的植被动态。MCS-LSTP 纳入多个关键驱动变量，并通过同质空间单元划分、单元级时间建模及连续表面重建，增强了对复杂时空依赖关系和空间异质性的综合表征能力。结果表明，与自回归积分滑动平均模型（ARIMA）、随机森林（RF）和 Transformer 模型相比，MCS-LSTP 模型在稳定性和预测精度方面表现更优。其平均群体稳定性指数（PSI）为 0.0572，98.7% 的预测单元 PSI 值低于 0.1，表明观测 NDVI 值与模拟 NDVI 值之间无显著偏差。测试集上的平均决定系数（R²）和均方根误差（RMSE）分别为 0.8551 和 0.2171。此外，MCS-LSTP 准确复现了 NDVI 的季节性波动和经向空间梯度，尤其在生长季期间，模拟值与参考值具有高度一致性。本研究为大尺度 NDVI 时空预测提供了稳健且可扩展的解决方案，并为黄河流域及其他生态脆弱区的生态风险评估提供了关键技术支撑。

Accurate prediction of vegetation dynamics is essential for understanding terrestrial ecosystems carbon and water cycles. However, existing Normalized Difference Vegetation Index (NDVI) prediction methods are limited in their ability to represent spatiotemporal heterogeneity and nonlinear interactions among multiple driving variables, which restrict predictive accuracy and extrapolation performance at large scales. To address these limitations, this study proposed a structured large-scale NDVI spatiotemporal prediction method incorporating multivariable constraints (MCS-LSTP) to characterize vegetation dynamics in complex and heterogeneous environments. MCS-LSTP incorporates multiple key driving variables and enhances the comprehensive representation of complex spatiotemporal dependencies and spatial heterogeneity through homogeneous spatial unit partitioning, unit-level temporal modeling, and continuous surface reconstruction. Results showed that MCS-LSTP model exhibited superior performance in terms of stability and prediction accuracy when compared to AutoRegressive Integrated Moving Average (ARIMA), Random Forest (RF), and Transformer models. The mean Population Stability Index (PSI) reached 0.0572, with 98.7% of prediction units exhibiting PSI values below 0.1, indicating no significant deviation between the observed and simulated NDVI values. The average R2 and RMSE on the test set were 0.8551 and 0.2171, respectively. Furthermore, MCS-LSTP accurately reproduced seasonal NDVI fluctuations and meridional spatial gradients, with particularly high agreement between simulated and reference values during the growing season. This study provides a robust and scalable solution for large-scale NDVI spatiotemporal prediction and offers critical technical support for ecological risk assessment in the Yellow River Basin and other ecologically vulnerable regions.