Leaf Biochemistry Estimation on EU High-Value Crops with ROSIS and DAIS Hyperspectral Data and Radiative Transfer Simulation

Abstract

This manuscript provides a description of progress made on the investigation of estimation of leaf biochemistry on high-value crops using hyperspectral remote sensing imagery. Hyperspectral optical indices related to leaf chlorophyll content were used to test different assumptions under open and row-crop canopies, specifically Olea europaea L. (olive trees) and Vitis vinifera L. (vineyards). Scaling-up methods were tested as a function of the different spatial resolutions of ROSIS and DAIS datasets acquired over two olive groves and ten vineyard fields in southern and northern Spain, respectively, during the HySens 2002 campaign. Leaf-level biochemical estimation from 1-m ROSIS and 5-m DAIS data required different modeling assumptions, enabling in some cases the use of PROSPECT-SAILH radiative transfer simulation when targeting olive-tree crowns. At lower spatial resolutions the soil and shadow scene components were considered through the linked PROSPECT-SAILH-FLIM models. These considerations along with the calculation of predictive equations using MCARI/OSAVI to minimize soil background variations in these canopies are also discussed.