Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model

In this study, the characteristic wavelengths of leaf biochemical parameters (including carotenoid content, chlorophyll ${a} + { b}$a+b content, dry matter content, equivalent water thickness, and leaf structure parameter) were obtained through a sensitivity analysis based on a physical model. Then, performance of the selected characteristic wavelengths for monitoring leaf biochemical contents (LBC) was analyzed by using the following six popular regression algorithms: random forest, backpropagation neural network, support vector regression, radial basic function neural network, partial least-squares regression, and Gaussian process regression of different parameter values/kernel functions/training functions. In addition, the optimal parameters of each regression algorithm for estimating LBC were determined. Lastly, the effect of different regression algorithms on the accuracy of LBC estimation using four different data sets was also discussed. The results demonstrated that the selected 10 characteristic wavelengths combined with the Gaussian process regression model can be efficiently applied in estimating LBC.