Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages.

Mairaj Din,Wen Zheng,Muhammad Rashid,Shanqin Wang,Zhihua Shi

doi:10.3389/fpls.2017.00820

Abstract

Hyperspectral reflectance derived vegetation indices (VIs) 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 VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 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 VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) 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 accounted for the sensitivity of the VIs 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 VIs under different N management practices.

Highlights

The application of remote sensing technology inprecision agriculture management has become increasingly prevalent among farmers due to its ability to optimize crop status by facilitating sound crop monitoring (Pei et al, 2014)
The results indicated that all of the indices were increasingly related to the temporal distribution of the leaf area index (LAI) data of 2015 and 2016 at different phenological stages, except for tillering and maturity, which showed the lowest R2 values for all of the indices and were excluded from the data
When the LAI exceeded saturation (LAI > 3 m2 m−2) at the booting stage, the normalized difference vegetation index (NDVI) leveled off (R2 = 0.67), and its sensitivity suffered while the modified soil-adjusted vegetation index (MSAVI) (R2 = 0.75) and MTVI2 (R2 = 0.77) have modifying factor to cope with it

Summary

Introduction

The application of remote sensing technology inprecision agriculture management has become increasingly prevalent among farmers due to its ability to optimize crop status by facilitating sound crop monitoring (Pei et al, 2014). Remote sensing can generate useful spectral reflectance data that provide rapid means for monitoring growth status through various biophysical, physiological, or biochemical crop parameters. The leaf area index (LAI), the one-sided green leaf area per unit ground surface area, is a key biophysical variable that is directly involved with canopy functioning processes, such as photosynthesis and respiration (Casa et al, 2012). It is a necessary parameter used by crop physiologists to remotely estimate canopy cover, crop growth and yield. It is functionally linked to the canopy spectral reflectance (Jin et al, 2013)

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Conclusion