Retrieval of leaf water content spanning the visible to thermal infrared spectra

Saleem Ullah*, Andrew K. Skidmore, Abel Ramoelo, Thomas A. Groen, Mohammad Naeem, Asad Ali

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390-14.0μm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R2) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR-SWIR), the most accurate spectral index yielded R2 of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R2 was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R2=0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R2=0.96, RMSE=4.74% and RMSE cross validation RMSECV=6.17%) followed by VNIR-SWIR reflectance spectra (R2=0.91, RMSE=6.90% and RMSECV=7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R2=0.67, RMSE=13.27% and RMSECV=16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content.

Original languageEnglish
Pages (from-to)56-64
Number of pages9
JournalISPRS Journal of Photogrammetry and Remote Sensing
Publication statusPublished - Jul 2014
Externally publishedYes


  • Water stress
  • Remote sensing
  • Visible-near infrared and shortwave
  • infrared (VNIR–SWIR)
  • Mid infrared (MIR)
  • Thermal infrared (TIR)
  • Statistical models
  • Visible-near infrared and shortwave infrared (VNIR-SWIR)


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