Monitoring of the Leaf Area Index of Cotton Based on Spectral Parameters and the Sensitivity Study
Xiuliang Jin
Abstract
Xiuliang Jin
Abstract
Leaf area index(LAI) is one of the key structural parameter for cotton canopy.The objectives of this study were to determine the relationships between spectral parameters and LAI so that the optimum regression models for estimating LAI were developed in cotton,and to analysis the sensitivity of these spectral parameters.The reflectance spectra of canopy were measured using a field radiometric spectrometer in different canopy LAI in the different growth stages of cotton.The results showed that the maximum sensitivity of reflectance to variation in leaf area index,694 nm and 1099 nm,were found in visible and near-infrared spectrum,respectively.Hence,previous established spectral parameters were modified using reflectance of these two wavebands.Furthermore,the models to retrieve LAI using wide dynamic range vegetation index(WDRVI) and ratio vegetation index(RVI) were most feasible with the maximum determination coefficients(r2)(0.8375 and 0.8324,respectively).Additional,RVI showed higher sensitivity to LAI than WDRVI consistently.
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Leaf area index(LAI) is one of the key structural parameter for cotton canopy.The objectives of this study were to determine the relationships between spectral parameters and LAI so that the optimum regression models for estimating LAI were developed in cotton,and to analysis the sensitivity of these spectral parameters.The reflectance spectra of canopy were measured using a field radiometric spectrometer in different canopy LAI in the different growth stages of cotton.The results showed that the maximum sensitivity of reflectance to variation in leaf area index,694 nm and 1099 nm,were found in visible and near-infrared spectrum,respectively.Hence,previous established spectral parameters were modified using reflectance of these two wavebands.Furthermore,the models to retrieve LAI using wide dynamic range vegetation index(WDRVI) and ratio vegetation index(RVI) were most feasible with the maximum determination coefficients(r2)(0.8375 and 0.8324,respectively).Additional,RVI showed higher sensitivity to LAI than WDRVI consistently.
Key concepts: Leaf area index, Canopy, Remote sensing, Sensitivity (control systems), Photochemical Reflectance Index, Vegetation (pathology), Range (aeronautics), Environmental science