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Imaging Spectrometry in Agriculture - Plant Vitality And Yield Indicators

  • Jan G. P. W. Clevers
Part of the Eurocourses: Remote Sensing book series (EURS, volume 4)

Abstract

For monitoring agricultural crop production, growth of crops has to be studied, e.g. by using crop growth models. Estimates of crop growth often are inaccurate for non-optimal growing conditions. Remote sensing can provide information on the actual status (e.g. its vitality) of agricultural crops. This information can be used to initialize, calibrate or update crop growth models, and it can yield parameter estimates to be used as direct input into growth models: (1) leaf area index (LAI), (2) leaf angle distribution (LAD) and (3) leaf colour (optical properties in the PAR region). LAI and LAD determine the amount of light interception. Leaf (or crop) colour influences the fraction of absorbed photosynthetically active radiation (APAR) and the maximum (potential) rate of photosynthesis of the leaves. A framework is described for integrating optical remote sensing data from various sources in order to estimate the mentioned parameters. Emphasis is on the importance of the red edge index as a measure for plant vitality. Imaging spectrometry data are needed for an accurate estimation of this red edge index.

The above concepts for crop growth estimation were elucidated and illustrated with a case study for sugar beet using groundbased and airborne data obtained during the MAC Europe 1991 campaign. A simple reflectance model was used for estimating LAI. Quantitative information concerning LAD was obtained by measurements at two viewing angles. The red edge index was used for estimating the leaf optical properties. Finally, a crop growth model (SUCROS) was calibrated on time-series of optical reflectance measurements to improve the estimation of beet yield.

Keywords

Sugar Beet Leaf Area Index Imaging Spectroscopy Solar Zenith Angle Leaf Chlorophyll Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© ECSC, EEC, EAEC, Brussels and Luxembourg 1994

Authors and Affiliations

  • Jan G. P. W. Clevers
    • 1
  1. 1.Dept. Landsurveying and Remote SensingWageningen Agricultural UniversityAH WageningenThe Netherlands

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