Inverting wheat leaf area index based on HJ-CCD remote sensing data and random forest algorithm

Abstract: The leaf area index (LAI) of crops is an important parameter for crop monitoring. With the remote sensing application in agriculture, inverting LAI of crops from remote sensing data has been studied. Among these studies, vegetation indices are widely used because they can reduce effect background noise on the spectral reflectance of plant canopies. In addition to using vegetation indices, modeling algorithm also plays an important role in improving the remote estimation accuracy of crop LAI. Recently, the emerging Random Forest (RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression. In this paper, we conducted studies on wheat LAI estimations utilizing RF algorithm and vegetation indices. Firstly based on China's environmental satellite charge-coupled device (HJ-CCD) image data of wheat (Triticum aestivum) from test sites in Jiangsu province of China during 2010-2013, fifteen vegetation indices from previously reported results and related LAI were respectively calculated at the jointing, booting, and anthesis stages. Then, through utilizing RF algorithm, the LAI inverting model for each stage was respectively established based on its vegetation indices and corresponding in situ wheat LAI measured during the HJ-CCD data acquisition. For each stage, the pooled data from 2010-2013 were randomly divided into a training dataset and an independent model validation dataset (75% and 25% of the pooled data, respectively). For the training dataset, the number of samples was 174 at jointing, 174 at booting, and 147 at anthesis. For the validation dataset, the number of samples was 58 at jointing, 58 at booting, and 49 at anthesis. The training dataset was used to establish models to predict wheat LAI during each growth stage, and the validation dataset was employed to test the quality of each prediction model. The RF model of each stage for estimating wheat LAI was then established in which the 15 vegetation indices were considered to be the independent variables and wheat LAI was the dependent variable. Additionally for each stage, the model based on artificial neural network (ANN) machine-learning algorithm was employed as a reference model, which had been successfully used to invert LAI of crops in previous studies. In order to evaluate each model's estimation accuracy and to further compare the performances of the two models for each stage, the coefficients of determination (R2) and the corresponding root mean square errors (RMSE) for the estimated-versus-measured LAI were calculated respectively on the basis of the corresponding validation data. The results indicated that RF outperformed ANN at each stage. For RF models, the R2 for the estimated-versus-measured LAI values for the three stages were 0.79, 0.67, and 0.59, respectively, in contrast to 0.57, 0.90, and 0.78 from RMSE. For ANN models, the R2 for the three stages was 0.67, 0.31, and 0.30, respectively, and the corresponding RMSE was 0.82, 1.94, and 1.43. Furthermore, RF showed the vegetation index of model that noticeably contributed to the LAI estimation for each stage (i.e., EVI at jointing, MTVI2 at booting, and MSR at anthesis). Thus, the RF algorithm provides an effective way to improve the prediction accuracy of LAI in wheat on a large scale.