Location: Aerial Application Technology ResearchTitle: Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter
|SUN, BO - Huazhong Agricultural University|
|WANG, CHUFENG - Huazhong Agricultural University|
|XU, BAODONG - Huazhong Agricultural University|
|KUAI, JIE - Huazhong Agricultural University|
|LI, XIAOYONG - Chinese Academy Of Agriculture & Mechanical Sciences|
|XU, SHIJIE - Huazhong Agricultural University|
|LIU, BIN - Huazhong Agricultural University|
|XIE, TIANJIN - Huazhong Agricultural University|
|ZHOU, GUANGSHENG - Huazhong Agricultural University|
|ZHANG, JIAN - Huazhong Agricultural University|
Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/18/2021
Publication Date: 5/26/2021
Citation: Sun, B., Wang, C., Yang, C., Xu, B., Kuai, J., Li, X., Xu, S., Liu, B., Xie, T., Zhou, G., Zhang, J. 2021. 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 Observation and Geoinformation. 102:1-10. https://doi.org/10.1016/j.jag.2021.102373.
Interpretive Summary: Leaf area index (LAI) is an important plant structural parameter and plays a vital role in evaluating crop growth and yield. In this study, canopy coverage derived from unmanned aerial vehicle (UAV) images was used as a correction parameter in a plant biophysical model to improve LAI estimation accuracy. Analysis results based on two years of field data showed the model incorporating canopy coverage correction had accurate and reliable performances in retrieving rapeseed LAI. This study provided a robust, practical, and low-cost method to accurately estimate LAI of rapeseed with UAV data.
Technical Abstract: 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. 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. Overall, the PROSAIL model was more robust over these two datasets. 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.