Comparing maize leaf area index retrieval from aerial hyperspectral images through radiative transfer model inversion and machine learning techniques

This study compares maize leaf area index (LAI) retrieval methods based on radiative transfer models and machine learning techniques. Ground LAI was measured from the study field at different growth stages where aerial hyperspectral images were acquired at the same stages. The PROSAIL-based model was built using a range of maize leaf and canopy parameters with a total of >21k simulations covering different maize spectral reflectance scenarios. Moreover, random forest and support vector machines were applied to the spectral and corresponding ground LAI measurements divided into 50% for model training and 50% for validation. Results showed that the PROSAIL-based model provided the highest R2 value between ground and estimated LAI followed by the RF and SVM where R2 values were 0.65, 0.59 and 0.35 respectively.