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

• DOI: 10.1007/s11119-019-09648-8
• Publication Date: 2019-03-16T10:02:45Z
• Authors: Wei Feng; Yapeng Wu; Li He; Xingxu Ren; Yangyang Wang; Gege Hou; Yonghua Wang; Wandai Liu; Tiancai Guo
• Keywords: Enhanced vegetation index; Linear relationship; Spectral index

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) LAI usually tend saturate under dense canopies production. purpose of this study was propose new VI which the estimating saturation greatly weakened, prediction accuracy improved conditions high winter wheat (Triticum aestivum L.). relationship ground-based canopy spectral reflectance investigated. results showed that optimized band combination, namely, form non-linear (NLI) more sensitive changes LAI. When λ(x1) = 798 nm λ(y2) 728 nm, combination NLI (798,728) had highest R2 0.757. Among common VIs, modified triangular 2 (MTVI2), ratio [RSI (760,730)] 2-band enhanced (EVI2) gave superior performance (R2 > 0.710) terms estimation, but were worse than (798,728). Inspired by (MNLI), further become novel (ONLI), can be calculated formula $${{\left( { 1 + 0} . 0 5} \right) \, \times \left( 0. 6\, \,R_{ 7 9 8}^{2} - 8} } \right)} \mathord{\left/ {\vphantom {{\left( \right)}}} \right. \kern-0pt} \right)}}$$ unified ONLI model an 0.779 root mean square error (RMSE) 1.013 across all datasets. These indicate has strong adaptability various cultivation provide good estimate wheat.