Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland

R. Darvishzadeh, A.K. Skidmore, M. Schlerf, C. Atzberger

Research output: Contribution to journalArticleAcademicpeer-review

317 Citations (Scopus)

Abstract

Radiative transfer models have seldom been applied for studying heterogeneous grassland canopies. Here, the potential of radiative transfer modeling to predict LAI and leaf and canopy chlorophyll contents in a heterogeneous Mediterranean grassland is investigated. The widely used PROSAIL model was inverted with canopy spectral reflectance measurements by means of a look-up table (LUT). Canopy spectral measurements were acquired in the field using a GER 3700 spectroradiometer, along with simultaneous in situ measurements of LAI and leaf chlorophyll content. We tested the impact of using multiple solutions, stratification (according to species richness), and spectral subsetting on parameter retrieval. To assess the performance of the model inversion, the normalized RMSE and R-2 between independent in situ measurements and estimated parameters were used. Of the three investigated plant characteristics, canopy chlorophyll content was estimated with the highest accuracy (R-2 = 0.70, NRMSE = 0.18). Leaf chlorophyll content, on the other hand, could not be estimated with acceptable accuracy, while LAI was estimated with intermediate accuracy (R-2 = 0.59, NRMSE = 0.18). When only sample plots with up to two species were considered (n = 107), the estimation accuracy for all investigated variables (LAI, canopy chlorophyll content and leaf chlorophyll content) increased (NRMSE=0.14, 0.16, 0.19, respectively). This shows the limits of the PROSAIL radiative transfer model in the case of very heterogeneous conditions. We also found that a carefully selected spectral subset contains sufficient information for a successful model inversion. Our results confirm the potential of model inversion for estimating vegetation biophysical parameters at the canopy scale in (moderately) heterogeneous grasslands using hyperspectral measurements. (C) 2008 Elsevier Inc. All rights reserved.
Original languageEnglish
Pages (from-to)2592-2604
JournalRemote Sensing of Environment
Volume112
Issue number5
DOIs
Publication statusPublished - 2008

Keywords

  • leaf-area index
  • canopy biophysical variables
  • remote-sensing data
  • reflectance data
  • optical-properties
  • neural-network
  • ancillary information
  • precision agriculture
  • satellite data
  • retrieval

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