Leaf area index (LAI), which is an important structural parameter, plays a vital role in evaluating crop growth and yield. In this study, we used the canopy coverage (CC) derived from unmanned aerial vehicle (UAV) images as a correction parameter in the PROSAIL model coupled with a neural network (NN) to improve the accuracy of LAI inversion of rapeseed plots. CC had a significantly positive impact on the accuracy of LAI inversion especially in sparse canopy structure with the 22.24% decrease in the entire dataset and 35.76% decrease in the sparse canopy dataset. We then compared the inversion performances of an empirical statistical model (ESM) based on a vegetation index and the PROSAIL model incorporating CC correction for 2016 and 2018 datasets. The ESM performed better in modeling the 2016 dataset, but its accuracy was much lower for the 2018 dataset (2016: NRMSE = 0.131; 2018: NRSME = 0.348). Overall, the PROSAIL model was more robust over these two datasets (2016: NRMSE = 0.152; 2018: NRMSE = 0.168). In addition, the original-resolution images were resampled to six coarse resolutions to evaluate the influence of image resolution on the LAI inversion performance of the PROSAIL model. When pixel size increased to more than 10 cm, the inversion accuracy began to decrease dramatically. In conclusion, introducing a canopy coverage correction parameter in the PROSAIL model improved its performance in retrieving rapeseed LAI.

https://doi.org/10.1016/j.jag.2021.102373