Leaf area index (LAI) is an important biophysical parameter and is a critical variable in many ecology models, productivity models, and carbon circulation studies. In the present study, we aimed to assess and compare various hyperspectral models in terms of their prediction power of tobacco LAI. In a pot experiment, tobacco canopy hyperspectral reflectance data of the root extending stage, fast growing stage, and mature stage under different water and nitrogen levels were collected with an ASD FieldSpec HandHeld spectroradiometer, Corresponding tobacco LAI values were also collected. LAI retrieval methods were evaluated using four vegetation indices: Ratio vegetation Index (RVI), Normalized difference vegetation index (NDVI), Modified soil-adjusted vegetation index (MSAVI) and Modified second triangular vegetation index (MTVI2), and using principal component analysis (PCA), and neural network (NN) methods. The LAI estimations of these methods were then compared. Results indicated that these methods can make robust estimates of LAI. Determination coefficients (R²) of the validated models of the vegetation indices, PCA, and NN were 0.768-0.852, 0.938 and 0.889, respectively. The PCA and NN methods showed higher precision. The stability of the PCA validated model is the best because its root mean square error (RMSE) of 0.172 is smaller than those of the vegetation indices (0.237-0.322) and NN (0.195). Among the vegetation indices, MTVI2 and MSAVI could remove the influence of soil and atmosphere and obtain better retrieval accuracy than either RVI or NDVI. Overall, the PCA and NN methods could improve retrieval precision and therefore be selected for LAI estimation.
The vegetation indices achieved a good level of accuracy in estimating tobacco LAI; however, as they generally use information from only a few wavelengths, model stability cannot be guaranteed. However, the regression model based on the vegetation indices does not require a large sample to be assured of accuracy within a certain range. The PCA method can effectively reduce the number of dimensions and retain the important information. The two main components transformed by PCA can be interpreted as the visible spectral factor and near-infrared spectral factor, which represented 95.71% of the variation in the hyper spectral data. PCA can make use of the complementary advantages of different spectral bands, and lower the random disturbance caused by some bands. This means that PCA can be a reliable and general method for LAI estimation. The neural network method has a strong nonlinear mapping ability, and does not require normally distributed data. The model can be effectively trained and tested when a large amount of data is available. Although the neural network model has a strong ability for linear and nonlinear fitting, it is unable to provide any insight on the power of potential explanatory variables to explain variation in the data. It is difficult to fully explain the decisions and processes of producing hyper spectral data output. At present, there are no specific rules that can be followed with the band combinations employed. Further study is required to understand the effects of integrating hyperspectral data of the bands after 1050 nm wavelength (for example, from 2500 nm) on the estimation of tobacco LAI with PCA or NN.