Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages - Din, Mairaj; Zheng, Wen; Rashid, Muhammad; Wang, Shanqin; Shi, Zhihua (Frontiers in Plant Science, 2017)
Hyperspectral reflectance derived vegetation indices are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral vegetation indices for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over two years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of vegetation indices and LAI values. Regression models were used to perform quantitative analyses between spectral vegetation indices and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine vegetation indices, the ratio vegetation index, normalized difference vegetation index, modified soil-adjusted vegetation index, modified triangular vegetation index and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent (NE) accounted for the sensitivity of the vegetation indices and MSAVI, MTVI2 and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived vegetation indices under different N management practices.