Estimating Winter Wheat Leaf Water Content by Combining UAV Spectral and Texture Features with Stacking Ensemble Learning

Leaf water content (LWC) is a vital physiological indicator reflecting crop water status, crucial for precision irrigation and water management. Traditional monitoring methods are labor-intensive and costly, while unmanned aerial vehicle (UAV) remote sensing offers an efficient alternative with high spatiotemporal resolution. This study developed an inversion model for winter wheat LWC based on a stacking ensemble learning framework integrating multispectral and texture features to improve estimation accuracy. UAV multispectral images collected at different growth stages were used to extract 17 vegetation indices (VIs) and 32 texture features (TFs). The top 10 features most correlated with LWC were selected to construct a fused dataset, and five machine learning models (SVM, RF, XGB, PLSR, RR) were combined within a base–meta stacking architecture. Results showed that: (1) Using only multispectral features yielded R2 values of 0.526–0.718 and rRMSE of 22.795–29.536%, while texture-only models performed worse (R2 = 0.273–0.425, rRMSE = 34.7–36.6%), indicating that single data sources cannot fully represent LWC variability. (2) Combining multispectral and texture features notably improved accuracy (R2 = 0.748–0.815; rRMSE = 18.5–21.6%), demonstrating the complementary advantages of spectral and spatial information. (3) Stacking ensemble learning outperformed all single models, achieving the highest precision under fused features (R2 = 0.865; rRMSE = 16.3%). (4) LWC distribution maps derived from the stacking model effectively revealed field-scale moisture differences and spatial heterogeneity during different periods. This study confirms that multi-source feature fusion combined with ensemble learning enhances UAV-based crop water estimation, offering a reliable and scalable approach for precision agricultural water monitoring.