A technique is presented for detecting vegetation crop nutrient stress from hyperspectral data. Experiments are conducted on peach trees. It is shown that nutrient deficiencies that caused stress can be detected reliably on hyperspectral spectra. During an extensive field campaign, foliar and crown reflectance has been measured with a portable field spectroradiometer. Airborne hyperspectral imagery is acquired over the orchard with the AHS hyperspectral sensor. The multi-level approach (leaf

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... el and top of canopy) enabled the assessment of vegetation indices and their relationship with pigment concentration at both leaf and canopy levels, showing the potential and limitations of hyperspectral remote sensing on the different levels. Stress on the peach orchard was treated with iron chelates to recover from iron chlorosis conditions. Blocks of trees treated with iron chelates created a dynamic range of chlorophyll concentration as measured in leaves. A relationship is obtained between the measured spectra and estimated biochemical parameters via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modeled values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the leaf level and Top of Canopy (TOC). Optimal band regions and bandwidths are analyzed.