LAI Inversion for Winter Wheat From UAV Hyperspectral Data Considering Crop Growth Heterogeneity

This is the authors' abstract. We don't add key points for this paper.

Leaf Area Index (LAI) is a key parameter for reflecting crop canopy structure and growth status, and crucial for precision agricultural management. Due to high acquisition costs and limitations of obtaining ground observation data, the radiative transfer models are widely used to generate simulated spectral data for inversion models. However, neglecting variations caused by factors like irrigation, fertilization, and soil differences may lead to some errors in inversion results. To address this problem, we proposed a stratified hybrid inversion method based on growth heterogeneity (GH-SHI). First, a simple linear iterative clustering algorithm was applied for superpixel-level image segmentation. Then, a growth status index was proposed to characterize the wheat growth heterogeneity and was used to classify the segmented regions into five winter wheat growth stages. For each category, simulated samples were generated with the stratified parameters ranges of PROSAIL to train BP neural network models and obtain LAI inversion results. Results show that the GH-SHI method achieves higher LAI inversion accuracy on the measured site datas compared with the traditional hybrid method, with about a 50% reduction in RMSE (from 0.86 to 0.40), an improvement in R² by 0.48 (from 0.13 to 0.61), and an increase in the correlation coefficient R by 0.2 (from 0.64 to 0.84). Meanwhile, the correlation between the Normalized Difference Vegetation Index (NDVI) and LAI was used to further validate the effectiveness of the proposed method. The results show that, compared to the traditional approach, the LAI estimated by the GH-SHI method exhibits a stronger correlation with NDVI, further confirming the reliability of its inversion performance. In summary, the proposed method not only improves the accuracy of LAI inversion, but also demonstrates adaptability to different growth stages and diverse crop conditions, showing potential for application in large-scale, site-free regions.