Leaf area index (LAI) is a vital input variable for crop growth and yield prediction models. Therefore, rapid and accurate crop LAI estimates can offer important information for monitoring and managing the quantity and quality of food production. Here, LAI values of maize and soybean were predicted applying height metrics and intensity metrics calculated through unmanned aerial vehicle (UAV) LiDAR data. Moreover, we compared the prediction performance of physical model with that of empirical model for estimating crop LAI. The physical model based on Beer–Lambert law yielded reliable estimation results using LiDAR height data (maize: R² = 0.815, RMSE = 0.385; soybean: R² = 0.627, RMSE = 0.515) and LiDAR intensity data (maize: R² = 0.719, RMSE = 0.474; soybean: R² = 0.548, RMSE = 0.567). However, the linear regression model obtained a higher estimation accuracy. The single linear regression model derived from LiDAR height data had an R² value of 0.837 (RMSE = 0.361) for maize and 0.658 (RMSE = 0.493) for soybean, and derived from LiDAR intensity data had an R² value of 0.749 (RMSE = 0.448) for maize and 0.460 (RMSE = 0.619) for soybean, respectively. We found that the random forest (RF) regression model yielded the lowest estimation accuracy in this study. Moreover, the RF regression model in our study was not able to reliably estimate soybean LAI whether using LiDAR height metrics (R² = 0.294) or intensity metrics (R² = 0.180). Our results show that both LiDAR intensity and height metrics are capable of reliably predicting maize and soybean LAIs, although LiDAR intensity data yielded lower estimation accuracy than LiDAR height data. In conclusion, the results presented in this study demonstrate that using UAV-LiDAR technology to predict crop LAI is a flexible, practical, and cost-effective method.

叶面积指数（LAI）是作物生长和产量预测模型的重要输入变量。因此，快速、准确的作物 LAI 估算可以为监测和管理粮食生产的数量和质量提供重要信息。在此，利用通过无人机 (UAV) LiDAR 数据计算的高度指标和强度指标来预测玉米和大豆的 LAI 值。此外，我们将物理模型的预测性能与估算作物 LAI 的经验模型的预测性能进行了比较。基于比尔-朗伯定律的物理模型利用激光雷达高度数据得出了可靠的估计结果（玉米：R ² = 0.815，RMSE = 0.385；大豆：R ² = 0.627，RMSE = 0.515）和LiDAR强度数据（玉米：R ² = 0.719，RMSE = 0.474；大豆：R ² = 0.548，RMSE = 0.567）。然而，线性回归模型获得了更高的估计精度。从 LiDAR 高度数据导出的单线性回归模型，玉米的 R ² 值为 0.837（RMSE = 0.361），大豆的 R ² 值为 0.658（RMSE = 0.493），而从 LiDAR 强度数据导出的单线性回归模型的 R ² 值为 0.837（RMSE = 0.361）。 b5> 玉米的值为 0.749 (RMSE = 0.448)，大豆的值为 0.460 (RMSE = 0.619)。我们发现随机森林（RF）回归模型在本研究中产生的估计精度最低。此外，无论使用LiDAR高度指标（R ² = 0.294）还是强度指标（R ² = 0.180），我们研究中的RF回归模型都无法可靠地估计大豆LAI。我们的结果表明，LiDAR 强度和高度指标都能够可靠地预测玉米和大豆 LAI，尽管 LiDAR 强度数据的估计精度低于 LiDAR 高度数据。 总之，本研究的结果表明，利用无人机激光雷达技术来预测作物 LAI 是一种灵活、实用且经济高效的方法。