Field Spectroscopy of Agricultural Crops

Marvin E. Bauer, Craig S.T. Daughtry, Larry L. Biehl, Edward T. Kanemasu, Forrest G. Hall

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

The design, implementation, and results of multisitemultiyear experiments to measure and model the multispectral reflectance of agricultural crops in relation to their biophysical characteristics are described. The experimental approach involved multitemporal reflectance measurements together with detailed measurements of the agronomic characteristics of crop canopies. One result of the field measurements and analyses was a quantitative description of the complex relationships among crop canopy, soil, atmosphere, and illumination and sensor geometries. Leaf area index was identified as a key biophysical parameter linking crop physiology and multispectral remote sensing. Quantitative understanding and models of this relationship led to the development of spectral-temporal profile models for crop species identification and development stage estimation. A second key development has been the development of conceptual approaches and models for spectral estimation of leaf area index and light interception of crop canopies as inputs to crop growth and yield models. Other results include quantification of the effects of soil background, cultural practices, moisture stress, and nutrient deficiencies on crop reflectance, and the effects of sun angle and sensor view angle on measured canopy reflectance. The field measurements of canopy reflectance and geometry also provided data bases to test and validate canopy radiation models. In summary, the AgRISTARS field research on agricultural crops has provided a critical link between satellite and leaf spectral data.

Original languageEnglish (US)
Pages (from-to)65-75
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
VolumeGE-24
Issue number1
DOIs
StatePublished - Jan 1986

Keywords

  • Multispectral
  • canopy radiation
  • crop identification
  • crop-condition assessment
  • leaf area index
  • reflectance
  • remote sensing
  • spectral inputs to crop models

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