LAI retrieval from multiangular image classification and inversion of a ray tracing model

A non-conventional approach for the estimation of leaf area index (LAI) and leaf angle distribution (LAD), based on the use of information contained in multiangular images and the inversion of a canopy ray tracing model, is proposed in this work. As an alternative to the use of overall image reflectance data, the image fraction components, i.e. sunlit and shaded leaves and soil, are obtained by supervised classification of ground-based multiangular images acquired using an inexpensive colour infrared camera, the Dycam ADC. These data are used for the inversion of a numerical model of a vegetation canopy in which the latter is described as composed of randomly distributed disks (leaves). The model was developed using the POV-ray ray tracer. Model inversion is carried out using the look-up-table approach. The proposed methodology was tested using an extensive data set gathered on the potato crop during experimental trials carried out at Viterbo (Italy) for 3 years. The results show that LAI was successfully estimated with a RMSE varying from 0.29 to 0.75 in the different years. The potential sources of error in both estimated and measured LAI values are extensively discussed.