Estimate leaf chlorophyll of rice using reflectance indices and partial least squares

Kang Yu, Martin Leon Gnyp, Lei Gao, Yuxin Miao, Xinping Chen, Georg Bareth

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

In this study field experiments were conducted to test the ability of optimized spectral indices and partial least squares (PLS) to estimate leaf chlorophyll (Chl) content of rice from non-destructive canopy reflectance measurements. We integrated techniques involving the optimization of narrow band spectral indices and the detection of red edge position to optimize one type of spectral indices, the ratio of reflectance difference index (RRDI), for the estimation of leaf Chl content. The optimized RRDI in the red-edge (RRDIre = (R745-R740)/(R740-R700)) accounted for 62% - 72% of the variation in leaf Chl content with an RMSE of 4.59 μg/cm2 - 4.89 μg/cm2. Compared to spectral indices, PLS improved the estimation of leaf Chl content, yielding R2 and RMSE of 0.85 μg/cm2 and 3.22 μg/cm2, respectively. Finally, the model based on RRDI and the PLS model were further validated by an independent dataset collected in farmer fields. RRDI and PLS models yielded acceptable accuracy with R2 of 0.49 and 0.55, respectively, and an RMSE of 5.47 μg/cm2 and 5.13 μg/cm2. Our results suggest the potential to optimize spectral indices and also the significance of PLS technique for mapping canopy biochemical variations.

Original languageEnglish (US)
Pages (from-to)45-54
Number of pages10
JournalPhotogrammetrie, Fernerkundung, Geoinformation
Volume2015
Issue number1
DOIs
StatePublished - Feb 1 2015

Keywords

  • Hyperspectral reflectance indices
  • Lambdaby-lambda band optimization
  • Leaf chlorophyll
  • Partial least squares (PLS)
  • Rice
  • Sanjiang Plain

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