Estimating canopy water content using hyperspectral remote sensing data

J.G.P.W. Clevers, L. Kooistra, M.E. Schaepman

Research output: Contribution to journalArticleAcademicpeer-review

118 Citations (Scopus)


Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow. PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015–1050 nm spectral interval and CWC (R2 = 0.97). For 8 plots at the floodplain site the spectral derivative over the 1015–1050 nm interval obtained with an ASD FieldSpec spectroradiometer yielded an R2 of 0.51 with CWC. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015–1050 nm interval with CWC. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC
Original languageEnglish
Pages (from-to)119-125
JournalInternational Journal of applied Earth Observation and Geoinformation
Issue number2
Publication statusPublished - 2010


  • leaf optical-properties
  • radiative-transfer models
  • imaging spectrometer data
  • dynamic vegetation model
  • reflectance data
  • indexes
  • retrieval
  • information
  • variables
  • products

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