Accurate above ground biomass (AGB) estimation is a key technology for crop growth monitoring and precision agriculture decision making. Aiming to address the two limitations of traditional unmanned aerial vehicle (UAV) remote sensing methods in cotton AGB estimation—models based on vegetation indices (VIs) were susceptible to the interference of canopy spectral saturation effects, and it was difficult to quantify the spatio-temporal heterogeneity of the dynamics of three-dimensional canopy structure and AGB accumulation—the spatial analysis of three-dimensional UAV point clouds and the temporal characteristics of canopy cover were integrated to construct a multi-dimensional estimation model based on plant height×canopy cover (PH×CC). By designing a comparative experimental framework, the performance differences between the PH×CC model and four types of traditional models were investigated: VIs combined with random forest (RF), gradient boosting (GB), support vector machine (SVM) and backpropagation neural network (BPNN) were systematically evaluated. The results showed that the PH×CC model had significant advantages on the test set. Its coefficient of determination of estimation accuracy (R2) was 0.93, and the root mean square error (RMSE) was 15.30g/m2, which was an improvement of 22.3% compared with that of the optimal traditional model (RF: R2=0.76, RMSE was 23.35g/m2) (P<0.01). The mechanism analysis showed that the PH×CC parameters can analyze 83% of the variation in canopy structure (only 57% for the traditional VIs model) by synergistically representing the dynamic coupling relationship between the vertical expansion of PH and the horizontal expansion of canopy width, significantly improving the model’s ability to explain the interaction mechanism between AGB and structure. The research result can provide a method to overcome the technical bottleneck of “spectral-structural” information fusion in UAV agricultural situation monitoring, and at the same time it can provide a quantifiable modelling tool to analyze the biological mechanism of cotton canopy growth dynamics.