A novel spectral index for estimation of relative chlorophyll content of sugar beet

Fast and non-destructive estimation of canopy chlorophyll content from hyperspectral sensing is essential to monitor the physiological status of vegetation or to estimate crop above ground nitrogen status. The objective of this study is to propose an optimal method for the relative chlorophyll content (SPAD) estimation of sugar beet canopy using ground-based hyperspectral imagery. Field experiments were conducted over three years at three different growth stages, across three different sites, using different cultivars and nitrogen (N) application rates. Quantitative correlations between SPAD value and canopy hyperspectral reflectance of sugar beet canopy after different pretreatment algorithms were established. Ten classical spectral indexes selected from the literature for estimating SPAD value in sugar beet canopy were evaluated and compared to a novel modified chlorophyll index (MCI) produced in this study by introducing a parameter to chlorophyll index (CI) to improve the estimation accuracy. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) were optimized by using all possible combinations of spectral bands from the range of 390 nm to 990 nm. The prediction performance of partial least squares (PLS) regression models for optimized indexes (e.g., NDVI, CI and MCI), compared to the corresponding classical spectral indexes (e.g., ND550, ND705, CIgreen and CIred edge) was examined. Results showed that standard normal variate transformation (SNV) was the best pretreatment method for the hyperspectral data of this study. Models resulted after bands combinations optimization were found to be more accurate than models developed using the classical spectral indexes. The performance of proposed spectral index, MCI (R747, R839), MCI (R861, R884) and MCI (R931, R770), were best for the prediction accuracy of SPAD value in sugar beet for the validation set with the coefficient of determination (R2) of 0.83, 0.70 and 0.75, the root mean square error (RMSE) of 2.37, 3.11 and 2.78, and the relative root mean square error (RRMSE) of 4.95%, 6.05% and 5.75%, for the rapid growth stage of leaf cluster, sugar growth stage and sugar accumulation stage, respectively. It can be concluded that the index proposed can be implemented for the prediction of SPAD value of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions