Estimation of leaf area index of sugarcane using crop surface model based on UAV image

Abstract: The red-green-blue (RGB) digital camera on unmanned aerial vehicle (UAV) with the relatively low cost and near real-time image acquisition renders a remote sensing platform, which is an ideal tool for crop monitoring in precision agriculture. Some successful applications have been made in biomass and yield estimation. However, retrieval of leaf area index (LAI) using plant height information extracted by crop surface models (CSMs) has been paid very limited attention to. Therefore, the objective of this study was to demonstrate the feasibility of estimating LAI with CSMs-based plant height. The study was conducted in warm and wet southern China where the sugarcane was planted widely. In this study, we acquired RGB imaging data of sugarcane in whole growing stage (8 flights) by this platform. Afterward, 42 ground control points (GCPs) were evenly distributed across the field due to the rugged terrain of the experimental area. The CSMs were built with the GCPs data and the UAV-based RGB image with very high resolution using the structure from motion (SfM) algorithm, and then the plant height information derived from CSMs was applied to estimate the LAI of sugarcane. The estimated LAI values were validated using the ground measurement data, which were collected simultaneously with the image acquisition. To assess the accuracy of plant height extracted from the CSMs without geo-referencing by GCPs data, we also constructed the ground elevation model by inverse distance weighted (IDW) interpolation to obtain plant height. In addition, we applied 6 visible band vegetation indices including green-red vegetation index (GRVI), normalized redness intensity (NRI), normalized greenness intensity (NGI), green leaf index (GLI), atmospherically resistant vegetation index (ARVI), and modified green-red vegetation index (MGRVI) from RGB image to predict the LAI, respectively. The performance of prediction models based on 6 vegetation indices was assessed by comparing with that based on plant height. The predicted plant heights based on GCPs geo-referenced CSMs matched well with the observations in the validation set, with the R2 value of 0.961 2 and the root mean square error (RMSE) of 0.215 2 at the 0.01 significance level. This result demonstrated that the UAV-based CSMs with geo-referencing by GCPs were more effective in monitoring the characteristics of sugarcane canopy over rugged terrain. In all the selected visible band vegetation indices, GRVI had the decent agreement with LAI prior to late elongation stage, with the R2 value of 0.779 0, the RMSE value of 0.556 1, and the mean relative error (MRE) of 0.168 0 in the validation set. In contrast, the plant height models showed a better performance than the visible band VIs over the same period, and the best estimate for LAI was obtained from CSMs-based plant height (R2=0.904 4, RMSE=0.366 2, and MRE=0.124 3). Unfortunately, due to that leaves turned to be withering since late elongation stage, all models in this study had relatively poor performance in estimating the LAI in the whole growing stage. NRI performed the best for the LAI estimation in the whole growing stage (R2=0.668 4, RMSE=0.636 0, and MRE=0.187 5), while its effect was poorer compared with the result before late elongation stage. Hence, it was unsuitable for LAI estimation from visible band VIs and plant height after late elongation stage. Furthermore, all above visible band VIs in this study were affected by the saturation phenomenon with varying degrees at high LAI levels. Conversely, the CSMs-based plant height model, which showed a linear trend without saturation at high LAI, proved to be the best predictor before late elongation stage. Because the key growing stage covered the period from seedling stage to late elongation stage, and the plant height models overcame the saturation limits of visible band VIs, it was better to estimate LAI with plant height. The results of this study indicate that using CSMs-based plant height to retrieve LAI of sugarcane in the important growth period is feasible. Moreover, since the excellent fitting of CSMs-based plant height to the ground observations, this technology is a powerful tool to obtain crop canopy features accurately and rapidly and provides a new approach to the crop condition monitoring in large areas.