Predicting foliar biochemistry of tea, Camellia sinensis, using reflectance spectra measured at powder, leaf and canopy levels

Meng Bian, Andrew K. Skidmore, Martin Schlerf, Tiejun Wang, Yanfang Liu, Rong Zeng, Teng Fei

Research output: Contribution to journalArticleAcademicpeer-review

52 Citations (Scopus)


Some biochemical compounds are closely related with the quality of tea (Camellia sinensis (L.)). In this study, the concentration of these compounds including total tea polyphenols, free amino acids and soluble sugars were estimated using reflectance spectroscopy at three different levels: powder, leaf and canopy, with partial least squares regression. The focus of this study is to systematically compare the accuracy of tea quality estimations based on spectroscopy at three different levels. At the powder level, the average r2 between predictions and observations was 0.89 for polyphenols, 0.81 for amino acids and 0.78 for sugars, with relative root mean square errors (RMSE/mean) of 5.47%, 5.50% and 2.75%, respectively; at the leaf level, the average r2 decreased to 0.46–0.81 and the relative RMSE increased to 4.46–7.09%. Compared to the results yielded at the leaf level, the results from canopy spectra were slightly more accurate, yielding average r2 values of 0.83, 0.77 and 0.56 and relative RMSE of 6.79%, 5.73% and 4.03% for polyphenols, amino acids and sugars, respectively. We further identified wavelength channels that influenced the prediction model. For powder and leaves, some bands identified can be linked to the absorption features of chemicals of interest (1648 nm for phenolic, 1510 nm for amino acids, 2080 nm and 2270 nm for sugars), while more indirectly related wavelengths were found to be important at the canopy level for predictions of chemical compounds. Overall, the prediction accuracies achieved at canopy level in this study are encouraging for future study on tea quality estimated at the landscape scale using airborne and space-borne sensors.
Original languageEnglish
Pages (from-to)148-156
JournalISPRS journal of photogrammetry and remote sensing
Publication statusPublished - 2013


  • Total tea polyphenols
  • Free amino acids
  • Soluble sugars
  • Tea quality
  • Field spectroscopy
  • Partial least squares regression


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