Inversion of maize leaf area index from UAV hyperspectral and multispectral imagery

The accurate estimation of Leaf area index (LAI) is of great importance for evaluating crop growth in precision agriculture. Although previous studies have confirmed great advantages of unmanned aerial vehicle (UAV) remote sensing for LAI estimation in the field, accurate, reliable and efficient LAI estimation with practical applications still faces challenges due to model limitations and variations in the spectral and spatial scales of UAV remote sensing. In this study, we constructed the hybrid inversion models (HIMs) for estimating maize LAI using UAV hyperspectral and multispectral data, respectively. The HIMs combines the advantages of radiative transfer models and machine learning regression algorithms, and are optimized by active learning (AL) algorithm. The results reveal that the inclusion of AL in the HIMs an effectively improve the accuracy of the model. The Gaussian process regression-based HIM with AL (GPR-AL-HIM) obtained the best performance in LAI estimation (R2 = 0.86, RMSE = 0.30 and NRMSE = 10.16 %). Meanwhile, GPR-AL-HIM was also determined to outperform the physical model based on the look-up-table (LUT) and the empirical statistical model based on vegetation indices. The model was validated with another independent dataset and also obtained a high accuracy (R2 = 0.84, RMSE = 0.23 and NRMSE = 11.78 %). In addition, we also explore the effects of the UAV spectral (multispectral and hyperspectral) and image spatial resolution on LAI inversion. The results reveal that the hyperspectral data exhibit an advantage over the multispectral data for LAI inversion using the GPR-AL-HIM. The accuracy of the GPR-AL-HIM decreased with increasing spatial resolution, but the accuracy varied less within a certain spatial resolution range (e.g., R2 of 0.86-0.84 and RMSE of 0.30-0.33 for hyperspectral images at 1.5-15 cm spatial resolution). Furthermore, the LAI distribution in the study area was accurately mapped using the GPR-AL-HIM with the hyperspectral and multispectral images, with the latter exhibiting lower uncertainties. The GPR-ALHIM is mainly aimed at maize, and in the future, we will explore the applicability of this model in other crops. This work provides a reference for the design of a monitoring scheme with crop parameters based on UAV remote sensing in precision agriculture.