An optimized non-linear vegetation index for estimating leaf area index in winter wheat

Using hyperspectral remote sensing technology to monitor leaf area index (LAI) in a timely, fast and non-destructive manner is essential for accurate quantitative crop management. The relationships between existing vegetation indices (VIs) and LAI usually tend to saturate under dense canopies in crop production. The purpose of this study was to propose a new VI in which the estimating saturation is greatly weakened, and prediction accuracy is improved under conditions of high LAI in winter wheat (Triticum aestivum L.). The quantitative relationship between ground-based canopy spectral reflectance and LAI in wheat was investigated. The results showed that the optimized band combination, namely, the form of non-linear vegetation index (NLI) was more sensitive to changes in LAI. When λ(x1) = 798 nm and λ(y2) = 728 nm, the band combination NLI (798,728) had the highest R2 of 0.757. Among the common VIs, the modified triangular vegetation index 2 (MTVI2), the ratio spectral index [RSI (760,730)] and the 2-band enhanced vegetation index (EVI2) gave superior performance (R2 > 0.710) in terms of LAI estimation, but were worse than NLI (798,728). Inspired by the modified non-linear vegetation index (MNLI), NLI (798,728) was further optimized to become a novel optimized non-linear vegetation index (ONLI), which can be calculated by the formula 1+0.05×0.6×R7982-R728/0.6×R7982+R728+0.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\left( { 1 { + 0} . 0 5} \right) \, \times \, \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, - \,R_{ 7 2 8} } \right)} \mathord{\left/ {\vphantom {{\left( { 1 { + 0} . 0 5} \right) \, \times \, \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, - \,R_{ 7 2 8} } \right)} { \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, + \,R_{ 7 2 8} { + 0} . 0 5} \right)}}} \right. \kern-0pt} { \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, + \,R_{ 7 2 8} { + 0} . 0 5} \right)}}$$\end{document}. The unified ONLI model gave an R2 of 0.779 and root mean square error (RMSE) of 1.013 across all datasets. These results indicate that the novel ONLI has strong adaptability to various cultivation conditions and can provide a good estimate of LAI in winter wheat.