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Leaf area index, plant height, and above-ground biomass are key physicochemical parameters that reflect a crop's growth status. Accurate and efficient quantitative estimation of these indicators is crucial for developing production management strategies and predicting yields. UAV remote sensing imagery offers advantages such as mobility, flexibility, and high data collection efficiency, making it practical for monitoring maize growth indicators. However, existing parameter inversion models are significantly affected by background noise such as soil and shading. Furthermore, the asymptotic saturation phenomenon in optical sensor observations limits the ability of spectral information to assess key crop parameters. This study aims to investigate whether background pixels can be removed from UAV imagery and whether utilising canopy thermal information extracted from UAV thermal infrared images can improve the accuracy of crop parameter inversion. First, UAV images were segmented using vegetation indices. Vegetation indices, texture features, and temperature characteristics were extracted for AllPix and GreenPix within the experimental plots. Subsequently, Correlation analysis and random forest-based feature selection were employed to screen the characteristic parameters. Finally, we employed multiple linear stepwise regression (MLSR), partial least squares regression (PLSR), random forest regression (RFR), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to establish LAI, PH, and AGB inversion models for four critical growth stages of spring maize with a stratified validation strategy. The results indicated that spring maize LAI, PH, and AGB showed the strongest correlation coefficients with MRETVI, percentage of hot spot area, and mean temperature, respectively, at −0.856, −0.781, and −0.858. The results indicated that the model developed by integrating AllPix and GreenPix features exhibited the best performance. The optimal validation set for LAI yielded determination coefficients of 0.8579, with root mean square errors of 0.7861 m²/m². For PH, the determination coefficients of the optimal validation set is 0.9605, with root mean square errors of 20.8327 cm. For AGB, the optimal validation sets yielded determination coefficients of 0.9094, with root mean square errors of 83.1961g. Compared with models based on all pixels, the models based on spring maize pixel provided higher estimation accuracy for LAI, PH, and AGB. In particular, for LAI, the validation R² increased by 0.0270 and the RMSE decreased by 0.0714 m²/m². For PH, the validation R² increased by 0.0490 and the RMSE decreased by 9.8603 cm. For AGB, the validation R² increased by 0.1219 and the RMSE decreased by 36.6957 g. These findings provide technical support for monitoring spring maize growth and optimising production management.