Estimating Winter Wheat LAI Using Hyperspectral UAV Data and an Iterative Hybrid Method

Abstract

Leaf area index (LAI) is an important indicator for crop growth monitoring. Due to the small number of ground-measured samples, the hybrid method, using the radiative transfer model (RTM) to generate simulated samples and combining with the regression model, is popular for LAI estimation. However, there is still difference between simulated spectrum and measured spectrum, which may affect the inversion results. In this study, an iterative hybrid method combines BP neural network, PROSAIL model and an optimal sample selection method for crop LAI estimation was proposed. A small number of ground-measured samples and unmanned aerial vehicle (UAV) hyperspectral data were used to estimate LAI firstly. Then the initial LAI result was used as the parameter of PROSAIL model to generate the simulated spectrum. The simulated spectrum with high similarity with the UAV spectrum and corresponding LAI value would be treated as new samples for BP neural network. After several iterations, a reasonable sample set was obtained to estimate winter wheat LAI. The method proposed in this study is evaluated using ground-measured test samples and compared with the common hybrid methods. Results indicate that with the increase of the number of training samples, the accuracy of estimation model is improved (RMSE/MAE decreased from 0.4685/0.0301 to 0.4377/0.0272, respectively, while R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> increased from 0.5857 to 0.6384). Also, the accuracy of proposed iterative hybrid model is higher than that of commonly used hybrid model. The experiments demonstrate the relatively high accuracy of the proposed iterative hybrid method, which could be used for vegetation parameter estimation with only a small number of ground samples.