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.

The authors gratefully acknowledge the HySens project support provided through the Access to Research Infrastructures EU Program. Financial support from the Spanish Ministry of Science and Technology (MCyT) for this project, and financial support to P.J. Zarco-Tejada under the MCyT “Ramón y Cajal” Program are also acknowledged.

3rd EARSeL WORKSHOP on IMAGING SPECTROSCOPY, pp. 597-602, Munich, Germany, 13-16 May 2003

Peer reviewed