Modelling biochemical processes in orchards at leaf- and canopy-level using hyperspectral data

This research was conducted to evaluate the potenti al and limitations of hyperspectral remote sensing to detect iron deficiency in capital-intensive multi-a nnual crop systems, e.g. peach orchards. The noted deficiency can be regarded as a proxy for deviation s from optimal plant functioning, while detection o f such deviations is in turn of significant importanc e to monitoring and modelling efforts of orchards a s production systems. Hyperspectral leaf, canopy, and airborne reflectance measurements were acquired in a peach ( Prunus persica L.) orchard in Zaragoza, Spain. Leaf- and canopy-l evel data were collected with a handheld spectroradiometer (ASD, Inc.), while the AHS-160 hyperspectral sensor provided airborne data. Blocks of trees were treated with different amount of iron chelates (Sequestrene) which created a dyna mic range of chlorophyll concentration as measured in l eaves. Hyperspectral measurements at leaf-level were carri ed out to characterize the physiological aspects of nutrient stress, as opposed to the evaluation of pl ant nutrient status at the complete plant-level. St ress- induced physiological changes make stress detection at the leaf-level possible at an early stage of su b- optimal photosynthetic functioning. Airborne imager y, however, is difficult to interpret due to alteri ng illumination angles, BRDF effects, and intervening atmospheric light interactions resulting in an alte ration of the vegetative reflectance spectrum. Although ma ny studies have implemented hyperspectral analysis of nutrient status at large scales, this research fiel d is still in its infancy phase, since the link bet ween airborne- and leaf-level measurements is lacking. This inevit ably makes the physiological interpretation of exis ting hyperspectral research more complex. The multi-level (leaf, canopy, and airborne) approach presented h ere enabled the assessment of vegetation indices and th eir relationship with pigment concentration at each monitoring level. Pertinent classical chlorophyll-r elated vegetation indices were tested and new indic es were extracted from the spectral profiles by means of band reduction techniques and narrow-waveband rationing, which involved all possible 2-band combi nations. Robustness was evaluated by studying the index performance for datasets of increasing comple xity, from leaf- to canopy- and airborne-level. Physiological interpretations extracted from leaf-l evel experiments were extrapolated to canopy- and airborne level. The measured spectra and estimated biochemical parameters were related 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 modelled reflectance values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking c hlorophyll to the reflectance measured at the leaf level and at the Top of Canopy (TOC), while optimal band regions and bandwidths also were analyzed.