Accurately and rapidly monitoring the spatio-temporal distribution of aboveground biomass in crops is crucial for assessing growth conditions and managing precision irrigation in agricultural fields. Spectral indices have been widely utilized for estimating aboveground biomass; however, the spectral saturation effect under high coverage significantly impacts model performance. Furthermore, the effectiveness of integrating complementary and synergistic effects of spectral, temperature, and texture information from UAV remote sensing for estimating maize aboveground biomass across different growth stages and irrigation treatments at the farmland scale remains unclear. Additionally, the influence of meteorological factors on the interannual performance of remote sensing models for aboveground biomass estimation requires further investigation. Based on data from experimental sites in Inner Mongolia in 2018, 2019, and 2021, the spectral indices, temperature indices, and texture information obtained from UAV-based multisource remote sensing system, along with meteorological parameters such as reference evapotranspiration and vapor pressure deficit, were used as input features. Five modeling methods—stepwise regression, random forest regression, adaptive boosting regression, support vector regression, and one-dimensional convolutional neural network regression—were employed to establish a multisource feature fusion remote sensing estimation model for aboveground biomass of maize in large fields. The results showed that compared with single-type remote sensing information, the aboveground biomass estimation model integrating spectral, temperature, and texture information had better accuracy. Among them, the one-dimensional convolutional neural network regression model had relatively better accuracy (R²=0.87, RMSE=295.77 g/m²). After introducing meteorological parameters, there was no significant improvement in the accuracy of the aboveground biomass estimation model by using drone multisource remote sensing information, indicating redundancy between meteorological parameters and drone multisource remote sensing information. Considering the cost of drone-mounted equipment and the operation efficiency, the aboveground biomass estimation model based on spectral indices and meteorological parameters using random forest regression also had satisfactory accuracy (R²=0.85, RMSE=296.74 g/m²), suggesting that meteorological parameters can serve as a supplement to spectral indices. The performance of the one-dimensional convolutional neural network model for aboveground biomass estimation varied across different growth stages, indicating that the accuracy and reliability of the model need to be comprehensively assessed based on the crop growth stage. The research result can provide technical support for promoting the application of drone multisource remote sensing technology in precision irrigation management in farmlands.