Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data

Rasmus Houborg*, Eva Boegh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

147 Scopus citations

Abstract

Reflectance data in the green, red and near-infrared wavelength region were acquired by the SPOT high resolution visible and geometric imaging instruments for an agricultural area in Denmark (56°N, 9°E) for the purpose of estimating leaf chlorophyll content (Cab) and green leaf area index (LAI). SPOT reflectance observations were atmospherically corrected using aerosol data from MODIS and profiles of air temperature, humidity and ozone from the Atmospheric Infrared Sounder (AIRS), and used as input for the inversion of a canopy reflectance model. Computationally efficient inversion schemes were developed for the retrieval of soil and land cover-specific parameters which were used to build multiple species and site dependent formulations relating the two biophysical properties of interest to vegetation indices or single spectral band reflectances. Subsequently, the family of model generated relationships, each a function of soil background and canopy characteristics, was employed for a fast pixel-wise mapping of Cab and LAI. The biophysical parameter retrieval scheme is completely automated and image-based and solves for the soil background reflectance signal, leaf mesophyll structure, specific dry matter content, Markov clumping characteristics, Cab and LAI without utilizing calibration measurements. Despite the high vulnerability of near-infrared reflectances (ρnir) to variations in background properties, an efficient correction for background influences and a strong sensitivity of ρnir to LAI, caused LAI-ρnir relationships to be very useful and preferable over LAI-NDVI relationships for LAI prediction when LAI > 2. Reflectances in the green waveband (ρgreen) were chosen for producing maps of Cab. The application of LAI-NDVI, LAI-ρnir and Cabgreen relationships provided reliable quantitative estimates of Cab and LAI for agricultural crops characterized by contrasting architectures and leaf biochemical constituents with overall root mean square deviations between estimates and in-situ measurements of 0.74 for LAI and 5.0 μg cm- 2 for Cab. The results of this study illustrate the non-uniqueness of spectral reflectance relationships and the potential of physically-based inverse and forward canopy reflectance modeling techniques for a reasonably fast and accurate retrieval of key biophysical parameters at regional scales.

Original languageEnglish (US)
Pages (from-to)186-202
Number of pages17
JournalRemote Sensing of Environment
Volume112
Issue number1
DOIs
StatePublished - Jan 15 2008

Keywords

  • AIRS
  • Atmospheric correction
  • Barley
  • Canopy reflectance model
  • Dry matter content
  • Green reflectance
  • Image-based application
  • Inverse modeling
  • Leaf area index
  • Leaf chlorophyll
  • Leaf mesophyll structure
  • MODIS
  • Maize
  • Markov clumping
  • NDVI
  • Near-infrared reflectance
  • SPOT
  • Spectral reflectances
  • Wheat

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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