Modeling Biochemical Processes in Orchards at Leaf and Canopy levels using Hyperspectral Data. 


Funding Institution:

Research Programme for Earth Observation "STEREO" (Support To The Exploitation And Research Of Earth Observation Data), Belgium Science Policy Office (BELSPO), Belgium. 2005-2006.   

Partners and Team Members:

•  Basjan Van Aardt (Coordinator), Pol Coppin , Stephanie Delalieux , Dimitry van der Zande , Katholieke Universiteit Leuven (KUL), Belgium.

•  Paul Scheunders, Steve De Backer , University of Antwerp (UA), Belgium.

•  Pieter Kempeneers , Flemish Institute for Technological Research (VITO), Belgium.

•  Pablo J. Zarco-Tejada , Oscar Pérez-Priego , Guadalupe Sepulcre-Cantó , Instituto de Agricultura Sostenible (IAS-CSIC), Córdoba, Spain.

•  Fermín Morales Iribas , Ruth Sagardoy , Estación Experimental Aula Dei (EEAD-CSIC), Zaragoza, Spain.

•  José Sobrino , Universidad de Valencia (UV), Valencia, Spain.

•  Remote Sensing Image Data collection by Instituto Nacional de Técnica Aeroespacial (INTA), funded by STEREO.


The potential yield of capital-intensive multi-annual crops (e.g., fruit orchards) is seldom realized in reality. This is caused by physiogenic aspects, pathogens (biotic stress), disadvantageous impact of abiotic stress to the plant (e.g., extreme temperature, dryness, high salinities), and improperly managed vegetative production systems.

A targeted monitoring and modelling of growth processes in such agricultural production systems will enable early detection and treatment of production limiting factors, with the goal of optimizing yield. Non-intrusive techniques are essential for capturing data in a continuous manner, thereby enabling rapid response and minimized unintentional impacts. Remote sensing lends itself to this purpose in that the structure and physiological status of a plant is represented by reflectance patterns. Incident energy is partly reflected, transmitted, and absorbed by the plant. The amount of reflected light depends on a number of leaf-related factors, such as external morphology, internal structure, concentration, and internal distribution of biochemical components, stress, etc.

Any opportunity to monitor these (and other) factors in an ongoing or periodic manner by means of remote sensing offers the potential to model plant production processes, and therefore also to steer the process by means of adapted management measures. This research proposal aims to investigate leaf- and canopy-level spectra, as well as radiative transfer-based methods to interpret AHS 160 high-spatial hyperspectral data to estimate leaf biochemical and canopy biophysical variables in peach (Prunus persica L.) orchards. Leaf-, canopy-and airborne-level hyperspectral data will be used to model specific biochemical constituents. Leaf chlorophyll a+b (Ca+b), dry matter (Cm), water (Cw), and leaf area index (LAI) are indicators of stress and growth that can be estimated by radiative transfer modeling from hyperspectral data in the 400-2500 nm spectral region. Nutritional deficiencies due to nitrogen (N), phosphorous (P), potassium (K), or iron (Fe) cause leaf chlorosis that may be mapped with remote sensing techniques.

  Data Access and Results