Remote estimation of crop fractional vegetation cover: the use of noise equivalent as an indicator of performance of vegetation indices

Abstract

Many algorithms have been developed for the remote estimation of vegetation fraction in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multispectral statistical approaches. The most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance, in the form of spectral vegetation indices. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the fractional vegetation cover in two crop types, maize and soybean, with contrasting canopy architectures and leaf structures. The noise equivalent of vegetation indices was used as an indicator of sensitivity and accuracy of vegetation fraction estimation. Among the indices tested, the enhanced vegetation index (EVI2), wide dynamic range vegetation index (WDRVI), green- and red-edge normalized difference vegetation index (NDVI) were found to be accurate in estimating vegetation fraction. These results were obtained using reflectance data acquired with close-range sensors (i.e. spectroradiometers mounted 6 m above the top of canopy). WDRVI was able to estimate vegetation fraction in both crops with no re-parameterization with RMSE below 6% and mean normalized bias below 2%.