Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter

International Journal of Applied Earth Observations and Geoinformation

Chufeng Wang¹  Jian Zhang¹  Baodong Xu¹  Jing Xie¹  Guangsheng Zhou²  Shijie Xu³  Tianjin Xie⁴  Chenghai Yang⁵  Bo Sun⁵  Xiaoyong Li⁵  Jie Kuai⁵  Bin Liu⁶ 

[1] Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan 430070, China;Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX 77845, USA;College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China;College of Science, Huazhong Agricultural University, Wuhan, China;Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China;Oil Crop Research Institute of Chinese Academy of Agricultural Sciences, Wuhan, China;

关键词: Leaf area index;    Rapeseed;    PROSAIL model;    Empirical statistical model;    Canopy coverage;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown