Comparing Different Methods for Wheat LAI Inversion Based on Hyperspectral Data

Abstract

Gaussian process regression (GPR) can effectively solve the problem of high-dimensional modeling with a small sample size. However, there is a lack of studies comparing GPR with other methods for leaf area index (LAI) inversion using hyperspectral data. In this study, winter wheat was used as the research material to evaluate performance of different methods for LAI inversion, i.e., GPR, an artificial neural network (ANN), partial least squares regression (PLSR) and the spectral index (SI). To this end, a 2-year water and nitrogen coupled experiment was conducted, and canopy hyperspectral and LAI data were measured at the critical growth stages of wheat. Based on these data, calibration and validation datasets were obtained, and the LAI prediction model was constructed using the above four methods and validated. The results showed that the LAI inversion models of the SI were the least effective compared with other methods, with R2 and RMSE ranging from 0.42–0.76 and 0.80–1.04 during calibration and R2 and RMSE ranging from 0.37–0.55 and 0.94–1.09 during validation. The ANN and GPR had the best results, with R2 of 0.89 and 0.85 and RMSE of 0.46 and 0.53 during calibration and R2 of 0.74 and 0.71 and RMSE of both 0.74 during validation. The PLSR had intermediate LAI inversion results, with R2 and RMSE values of 0.80 and 0.61 during calibration and R2 and RMSE values of 0.67 and 0.80 during validation. Thus, the ANN and GPR methods were recommended for LAI inversion of winter wheat.