LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

R. Darvishzadeh, A.K. Skidmore, M. Schlerf, C. Atzberger, F. Corsi, M.A. Cho

Research output: Contribution to journalArticleAcademicpeer-review

282 Citations (Scopus)

Abstract

The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and LCC. We tested the utility of univariate techniques involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, including stepwise multiple linear regression and partial least squares regression. Among the various investigated models, CCC was estimated with the highest accuracy (Rcv2 = 0.74, nRMSEcv = 0.35). All methods failed to estimate LCC (Rcv2 ¿ 0.40), while LAI was estimated with intermediate accuracy (Rcv2 values ranged from 0.49 to 0.69). Compared with narrow band indices and red edge inflection point, stepwise multiple linear regression generally improved the estimation of LAI. The estimations were further improved when partial least squares regression was used. When a subset of wavelengths was analyzed, it was found that partial least squares regression had reduced the error in the retrieved parameters. The results of the study highlight the significance of multivariate techniques, such as partial least squares regression, rather than univariate methods such as vegetation indices in estimating heterogeneous grass canopy characteristics
Original languageEnglish
Pages (from-to)409-426
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume63
Issue number4
DOIs
Publication statusPublished - 2008

Keywords

  • leaf-area index
  • radiative-transfer models
  • multiple linear-regression
  • resolution satellite data
  • band vegetation indexes
  • remote-sensing data
  • red edge position
  • nitrogen status
  • reflectance data
  • canopy reflectance

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