Abstract
The leaf area index (LAI) is not only an important parameter for monitoring crop growth, but also an important input parameter for crop yield prediction models and hydrological and climatic models. Several studies have recently been conducted to estimate crop LAI using unmanned aerial vehicle (UAV) multispectral and hyperspectral data. However, there are few studies on estimating the LAI of winter wheat using unmanned aerial vehicle (UAV) RGB images. In this study, we estimated the LAI of winter wheat at the jointing stage on simple farmland in Xinjiang, China, using parameters derived from UAV RGB images. According to gray correlation analysis, UAV RGB-image parameters such as the Visible Atmospherically Resistant Index (VARI), the Red Green Blue Vegetation Index (RGBVI), the Digital Number (DN) of Blue Channel (B) and the Green Leaf Algorithm (GLA) were selected to develop models for estimating the LAI of winter wheat. The results showed that it is feasible to use UAV RGB images for inverting and mapping the LAI of winter wheat at the jointing stage on the field scale, and the partial least squares regression (PLSR) model based on the VARI, RGBVI, B and GLA had the best prediction accuracy (R2 = 0.776, root mean square error (RMSE) = 0.468, residual prediction deviation (RPD) = 1.838) among all the regression models. To conclude, UAV RGB images not only have great potential in estimating the LAI of winter wheat, but also can provide more reliable and accurate data for precision agriculture management.
Highlights
The leaf area index (LAI), defined as the total one-sided leaf area per unit of surface area [1], leaf projection area [2] or half of the total interception area per unit of surface area [3], is an important parameter in controlling the physiological process of the vegetation canopy, which is closely related to biomass and crop yield
It maintains the advantages of the Visible Atmospherically Resistant Index (VARI) to a certain extent and shows a high sensitivity compared to winter wheat LAI
The coefficients of determination, R2 = 0.707 for the quadratic polynomial model based on VARI and R2 = 0.776 for the partial least squares regression (PLSR) model, illustrated that the existing studies on estimating the LAI of soybean breeding materials, winter wheat and rice by only using unmanned aerial vehicle (UAV) RGB-based image parameters [13,15,36]
Summary
The leaf area index (LAI), defined as the total one-sided leaf area per unit of surface area [1], leaf projection area [2] or half of the total interception area per unit of surface area [3], is an important parameter in controlling the physiological process of the vegetation canopy, which is closely related to biomass and crop yield. The accurate and rapid estimation of crop LAI is conducive to better crop monitoring, and conducive to its application in modeling, overall crop management and precision agriculture. The traditional LAI measurement method can obtain more accurate data, it is time-consuming and labor-intensive, and it is difficult to achieve large-scale overall monitoring [4]. Satellite observations used to obtain vegetation indices (VIs) contribute to the retrieving of crop LAI on earth, which is essential for achieving sustainable agriculture management. Studies conducted by Li et al [13] showed that the four VIs (the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), the enhanced vegetation index (EVI), and the 2-band enhanced vegetation index (EVI2)) derived from three different sensors (GF-1, HJ-1, Landsat-8) are all highly correlated with the LAI of winter wheat, and the spatial resolution must be considered in practical applications
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