Abstract
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 {{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)}}. 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.
Highlights
The leaf area index (LAI) is defined as the ratio of the one-sided surface area of all green leaves to the surface area per unit of land (Bréda 2003)
Except for low N rates, the optimized non-linear vegetation index (ONLI) R2 values were all > 0.70 (Fig. 8). These results indicate that the novel index ONLI has strong adaptability to various conditions and can give good estimates of LAI for winter wheat across different field factors
Remote sensing technology is a very important tool for the non-destructive, real-time monitoring of plant growth but the saturation of common vegetation indices (VIs) seriously limits the wide application of remote sensing technology to crop production management
Summary
The leaf area index (LAI) is defined as the ratio of the one-sided surface area of all green leaves to the surface area per unit of land (Bréda 2003). There are many methods for obtaining LAI, and these are mainly divided into two categories: (1) traditional direct measurement and (2) indirect measurement. The indirect measurement methods include the inclined point quadrat, the remote sensing method and optical methods using the LAI-2000 canopy analyser or other instruments (Wilson 1960; Arias et al 2007; Casas et al 2014). It is convenient and quick to obtain the LAI of vegetation by an indirect method, but such measurements are affected by cultivation factors and atmospheric conditions and still need to be corrected by the results obtained from a direct method (Chason et al 1991). The retrieval of LAI from remote sensing data is a popular and promising way to manage crops
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