Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data

Leaf area index (LAI) is one of the most important plant parameters when observing agricultural crops and a decisive factor for yield estimates. Remote-sensing data provide spectral information on large areas and allow for a detailed quantitative assessment of LAI and other plant parameters. The present study compared support vector regression (SVR), random forest regression (RFR), and partial least-squares regression (PLSR) and their achieved model qualities for the assessment of LAI from wheat reflectance spectra. In this context, the validation technique used for verifying the accuracy of an empirical-statistical regression model was very important in order to allow the spatial transferability of models to unknown data. Thus, two different validation methods, leave-one-out cross-validation (cv) and independent validation (iv), were performed to determine model accuracy. The LAI and field reflectance spectra of 124 plots were collected from four fields during two stages of plant development in 2011 and 2012. In the case of cross-validation for the separate years, as well as the entire data set, SVR provided the best results (2011: R-cv(2) = 0.739, 2012: R-cv(2) = 0.85, 2011 and 2012: R-cv(2) = 0.944). Independent validation of the data set from both years led to completely different results. The accuracy of PLSR (R-iv(2) = 0.912) and RFR (R-iv(2) = 0.770) remained almost at the same level as that of cross-validation, while SVR showed a clear decline in model performance (R-iv(2) = 0.769). The results indicate that regression model robustness largely depends on the applied validation approach and the data range of the LAI used for model building.