基于无人机高光谱数据的玉米叶面积指数估算

无人机高光谱遥感是低成本、高精度获取精细尺度农作物生物物理参数和生物化学参数的新型手段，以此快速反演叶面积指数（Leaf Area Index, LAI）对作物长势评价、产量预测具有重要意义。以山东禹城市玉米为研究对象，利用PROSAIL辐射传输模型模拟玉米冠层反射率获取LAI特征响应波段结合相关性定量分析获取对LAI变化最为敏感的波段，并以此计算6种植被指数（Vegetation Index,VI），利用6种回归模型分别对单一特征波段和VI进行反演建模，以实测LAI评定模型精度。研究表明，光谱反射率中516、636、702、760和867 nm等波段对LAI变化最为敏感，以此建立的单一特征波段反演模型预测LAI精度R2为0.44~0.58；RMSE为0.16~0.18，其中636 nm建立的模型(LAI=21.86exp(-29.47R₆₃₆))相比其他反演模型预测精度较高（R2=0.58，RMSE=0.16）；6种植被指数与LAI高度相关，相关性系数R ²为0.85~0.86，以此建立的反演模型相比单一特征波段反演模型精度有所提高，R2为0.66~0.72，RMSE为0.12~0.14；其中mNDVI构建的LAI估算模型（LAI=exp(2.76~1.77/mNDVI))精度最高（R2=0.72，RMSE=0.13）。无人机高光谱遥感是快速、无损监测农作物生长信息的有效手段，为指导精细化尺度作物管理提供依据。

Estimation of Corn Leaf Area Index based on UAV Hyperspectral Image

UAV hyperspectral remote sensing is a new means of low-cost, high-precision acquisition of fine-scale crop biophysical parameters and biochemical parameters, so that the rapid inversion of Leaf Area Index (LAI) has a crop growth assessment and yield prediction. Taking the corn of Shandong Yucheng as the research object, using the PROSAIL radiation transmission model to simulate the corn canopy reflectivity to obtain the LAI characteristic response band，combining correlation quantitative analysis to obtain the most sensitive band for LAI changes, and calculating the 6 vegetation index (VI). Inversion models were modeled on a single sensitive band and VI using six regression models to measure the accuracy of the model by LAI.Studies have shown that the spectral reflectance of 516nm, 636nm, 702nm, 760nm, 867nm are most sensitive to LAI changes, and the single-band inversion model established to predict LAI accuracy (R ²=0.44~0.58; RMSE=0.16~0.18).The model established by 636nm (LAI=21.86exp(-29.47R₆₃₆)) has higher prediction accuracy than other inversion models (R ²=0.58, RMSE=0.16); The 6 vegetation indexes are closely related to LAI with correlation at a significant level(R ²=0.85~0.86). The accuracy of the established inversion model is improved compared with the single characteristic band inversion model (R ²=0.66~0.72,RMSE=0.12~0.14);The LAI estimation model (LAI=exp(2.76~1.77/mNDVI)) constructed by mNDVI has the highest accuracy (R ²=0.72, RMSE=0.13). UAV hyperspectral remote sensing is an effective means for rapid and non-destructive monitoring of crop growth information, and provides a basis for guiding fine-scale crop management.