Analysis of vegetation indices derived from hyperspectral reflection measurements for estimating crop canopy parameters of oilseed rape (Brassica napus L.)

Vegetation indices (VIs), which are derived from hyperspectral measurements, may be useful non-destructive measures to estimate crop canopy parameters. A systematic analysis of the reflectance spectrum of winter oilseed rape (OSR) for the derivation of Vis has not been conducted yet. We therefore derived in our study VIs from 61 available wavebands of the spectral range from 400 nm to 1000 nm systematically and compared the best ones to commonly used indices. Hyperspectral reflectance and destructive measurements of crop canopy parameters were therefore carried out in 2005 and 2006 in northern Germany for calibration and in 2006 for validation at the same location. For the derivation of VIs for OSR, three different approaches were tested. The approaches differed in the way of the waveband combinations by combining two wavebands in a simple ratio (SR) form lambda(1)/lambda(2), a normalized difference index (NDI) form (lambda(1) - lambda(2))/(lambda(1) + lambda(2)) or by using a stepwise forward multiple regression (MR), which identifies the best linear combination of all available bands in a linear combination. The derived Vis were tested for their predictive power for crop canopy parameters like green area index (GAI), shoot dry matter (DMshoot) and total nitrogen amount in the shoot (N-shoot) and were compared to commonly used indices. Waveband combinations of two near infrared bands resulted in the best prediction of the tested crop canopy parameters for calibration and validation data sets. Correlation coefficients (r(2)) yielded values up to 0.82 between new indices and N-shoot. Especially, NDI 750,740 was best predicting GAI, whereas either NDI or SR forms of 740 nm and 780 nm showed best results predicting DMshoot and N-shoot and outperformed commonly used indices. Predicting crop canopy parameters by MR showed good results for calibration, but highest variation for validation among all newly derived indices. (C) 2008 IAgrE. Published by Elsevier Ltd. All rights reserved.