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Rapid, nondestructive and accurate monitoring of crop biomass is of great significance for crop productivity estimation and intelligent management. In order to explore the feasibility of monitoring crop biomass with light detection and ranging (LiDAR), LiDAR point cloud height metrics and aboveground biomass were obtained from field trials at key growth stages of wheat. Then based on the power function regression and support vector regression, the tenfold crossvalidation method was used to pick features and construct models, and the optimal wheat aboveground biomass monitoring models for whole growth period were selected respectively. Finally, the prediction abilities of the two models were tested and compared on the test set. The results showed that the support vector regression model constructed by the H95 and growth period provided the highest accuracy with an R2 being as high as 0.814 on training set, and its test results were with R2 of 0.821, RMSE of 1.730t/hm2, and RRMSE of 32.77%, which indicated that the model possessed good accuracy and adaptability. The power function regression model constructed by Hmean provided an R2 of 0809, and its test results were with R2 of 0.815, RMSE of 1.760t/hm2, and RRMSE of 33.33%, which also indicated that the model possessed good accuracy and adaptability. Estimation of wheat biomass by a height metric had inherent limitations, and the two models were more suitable for monitoring the aboveground biomass of wheat values less than 10t/hm2. On the whole sample set with aboveground biomass exceeding 10t/hm2, 95% of the predicted values of the models were underestimated and RMSE was increased exponentially. The feature of growth period was helpful to improve the prediction accuracy of the monitoring model.