Reducing background effects in orchards through spectral vegetationindex correction

Satellite remote sensing provides an alternative to time-consuming and labor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. Inorchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass andshadow), hampering the estimation of these biophysical variables. Furthermore, variable backgroundmixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row.Current correction methodologies use spectral differences between canopies and background, but strug-gle with a vegetated orchard floor. This background influence and the lack of a generic solution areaddressed in this study.Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive tochlorophyll content, water content and leaf area index. Afterwards, traditional background correctionmethods (i.e. soil-adjusted vegetation indices and signal unmixing) were compared to the proposedvegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree coverfraction) to rescale the vegetation indices accordingly and was applied to synthetic and WorldView-2satellite imagery. Through the correction, the effect of background admixture for vegetation indices wasreduced, and the estimation of biophysical variables was improved (