Characterizing canopy structural complexity for the estimation of maize LAI based on ALS data and UAV stereo images

The canopy structure of crops is a fundamental attribute of agro-ecosystems, providing efficient indications of the growing status, water stress, early detection of plant diseases, and yield estimation. In this study, we investigated the potential of point clouds generated from airborne laser scanning (ALS) and stereo images from an unmanned aerial vehicle (UAV) to characterize the structural complexity of maize canopies. The simultaneous collection of point clouds and field measurements were conducted on three sampling dates. A group of metrics that are often used in forest studies was calculated to quantify the structural complexity of canopies, which was further used to estimate the leaf area index (LAI) of maize. Stepwise linear regression models were established based on the metrics and LAI using the data sampled at single and mixed sampling dates, respectively. Results showed that significantly high correlations were found between the LAI values of maize and complexity metrics with a Pearson correlation coefficient (r) greater than 0.60. The leave one-out cross-validation (LOOCV) of LAI estimation showed that the highest robustness (RMSE = 0.16, rRMSE = 5.63%) was obtained by the model that was established from the overall data set, which explained 75% of the variation in the field-measured LAI. To conclude, the metrics of canopy structural complexity can be powerful predictors in the estimation of maize LAI based on our data set, which provides some new ideas for the study of precision agriculture using remote sensing.