IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2020)

Improvement of Leaf Chlorophyll Content Estimation Using Spectral Indices From Nonpolarized Reflectance Factor in the Laboratory and Field

  • Yuefeng Li,
  • Zhongqiu Sun,
  • Shan Lu,
  • Kenji Omasa

DOI
https://doi.org/10.1109/JSTARS.2020.3004976
Journal volume & issue
Vol. 13
pp. 3669 – 3682

Abstract

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Optical properties of light reflected from leaves can be described by both intensity and polarization, however, most studies focused on the intensity in the estimation of plant leaf biochemical parameters. In this study, multiangular photometric and polarimetric measurements of leaves from three different plant species are first performed in laboratory to estimate leaf chlorophyll content (LCC) using spectral indices at different viewing zenith angles. Based on the Stokes parameters, the spectral indices in terms of the I parameter reflectance factors measured in laboratory (IpRFlab if polarizer extinction is considered) can be used to estimate LCC, which has a similar accuracy as bidirectional reflectance factor (BRF); and the nonpolarized spectral proportion [the reduction of bidirectional polarized reflectance factor (BPRF) from IpRF (IpRF-BPRF)] improves the ability of the spectral indices, including single wavelength, simple ratio, simple difference and normalized difference indices, along with some other indices to estimate LCC using multiangular measurements. Subsequently, the field photometric and polarimetric measurements of leaves further confirm that the nonpolarized proportion improves the estimation of LCC for some spectral indices. These results not only provide evidence that the IpRFlab and IpRFfield taken from polarimetric measurements can be considered as the proxy of photometric measurements (BRF and HDRF) in both laboratory and field but also open the possibility to improve the accuracy of LCC estimation using a nonpolarized spectral reflectance factor from multiangular polarimetric measurements. These findings indicate that polarized remote sensing may play a significant role in vegetation studies.

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