Trend-decoupled district-adaptive ensemble learning for interpretable wheat yield prediction using Earth observation and climate data
This study presents a trend-decoupled, district-adaptive ensemble learning framework that integrates Earth observation and climate data to significantly improve the accuracy and interpretability of winter wheat yield forecasting in Pakistan's Multan Division, particularly under conditions of short historical records and non-stationary climate trends.