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This article in SSSAJ

  1. Vol. 73 No. 5, p. 1715-1723
     
    Received: Sept 13, 2008
    Published: Sept, 2009


    * Corresponding author(s): dan.long@ars.usda.gov
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doi:10.2136/sssaj2008.0288

Sensitivity of Ground-Based Remote Sensing Estimates of Wheat Chlorophyll Content to Variation in Soil Reflectance

  1. J. U. H. Eitela,
  2. D. S. Long *b,
  3. P. E. Gesslera,
  4. E. R. Huntc and
  5. D. J. Brownd
  1. a Geospatial Lab. for Environmental Dynamics University of Idaho, Moscow, ID 83844
    b Columbia Plateau Conservation Research Lab., PO Box 370 Pendleton, OR 97801
    c USDA-ARS Hydrology and Remote Sensing Lab., Bldg 007, Rm 104, BARC-West 10300 Baltimore Avenue Beltsville, MD 20705
    d Dep. of Crop and Soil Sciences Washington State Univiversity, Pullman 99164

Abstract

Spectral indices (SI) derived from crop reflectance data are sensitive to chlorophyll a and b content (Chl). However, the SI–Chl relationship might be confounded by variation in leaf area index (LAI) and soil background reflectance, especially in semiarid environments where water determines crop growth. This study evaluated the sensitivity of SI to variation in soil reflectance and how this may affect overall SI performance for ground-based sensing of Chl in dryland wheat (Triticum aestivum L.). Selected SI were computed from spectra simulated by the PROSPECT-SAIL radiative-transfer model for 5 LAI values, 7 Chl values, and 121 dry soil surface reflectance spectra. These spectra represented soils across major wheat growing areas in the United States. Soil properties and reflectance varied widely among the soils indicated by the high SI variation for LAI values < 1.5. Overall, soil background variation contributed less to the observed SI variability (<6%) than LAI (<97%). Combined indices [i.e., Normalized Difference Red Edge Index (NDRE)/Normalized Difference Vegetation Index (NDVI) and Modified Chlorophyll Absorption Ratio Index (MCARI)/Second Modified Triangular Vegetation Index (MTVI)] were least affected by soil background variation than single indices (i.e., NDVI). Results showed that ground sensing of Chl may be improved by means of combined indices that are resistant to soil background and LAI. Empirical measurements verified that the modeling results were a reliable representation of the influence of Chl, LAI, and soil on canopy reflectance. Further research is needed to evaluate the effect of soil moisture, surface roughness, residue, growth stage, and shadow on SI.

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