A Universal Maize Yield Estimation Framework: Integrating Multi-Dimensional Environmental Features to Mitigate the Impacts of Contrasting Inter-Annual Hydrothermal Variability

To address yield uncertainties from contrasting hydrothermal events in black soil regions, this study developed a universal estimation framework integrating multi-dimensional features. The universal yield estimation framework leveraged data from contrasting flood (2024) and drought (2025) scenarios in Youyi Farm in the Northeast Black Soil Region. And we fused multi-dimensional environmental features, including remote sensing, soil, and micro-topography factors, to identify “Regime Shifts” in yield-driving mechanisms across contrasting years. We evaluated four ML algorithms (RF, XGBoost, MLP, and TabNet) using Recursive Feature Elimination with Cross-Validation (RFECV) for variable optimization. Results showed the following: (1) The Universal RF model achieved superior robustness (R2 = 0.80), overcoming inter-annual fluctuations. (2) Mechanistic analysis identified a “Regime Shift” in yield drivers, transitioning from micro-topography-governed “drainage limitation” during flooding to soil-texture-dominant (SAND) “linear limitation” during drought. (3) Dynamic growth-stage differential features successfully corrected asymmetric spectral responses, resolving slope inversion and overestimation driven by “non-productive greenness” during flooding. (4) Spatio-temporal yield mapping revealed a transition from topography-constrained linear distributions (2024) to soil-moisture-driven “patchy mosaic” structures (2025). Moran’s I increased from 0.21 to 0.45, reflecting intensified yield clustering and intensified spatial clustering under drought. This study provides a robust tool for food security monitoring and site-specific management in climate-vulnerable intensive agricultural zones.