Exploring vegetation photosynthetic light-use efficiency using hyperspectral data

Liangyun Liu; Quanjun Jiao; Dailiang Peng

doi:10.1117/12.971189

19 October 2012 Exploring vegetation photosynthetic light-use efficiency using hyperspectral data

Liangyun Liu, Quanjun Jiao, Dailiang Peng

Proceedings Volume 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV; 85311C (2012) https://doi.org/10.1117/12.971189
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

Abstract

Photosynthetic light-use efficiency (LUE) is an important indicator of plant photosynthesis, but assessment by remote sensing needs to be further explored. In principle, chlorophyll fluorescence combined with heat dissipation is an expression of the balance between light harvesting (absorption) and light utilization in the photosynthetic process. The aim of the present study was to examine the above principles using solar-induced chlorophyll fluorescence (ChlF) and photochemical reflectance index (PRI), which is sensitive to dynamic changes in the xanthophyll cycle. LUE-ChlF models were developed for ChlF at 688 nm (R² = 0.72) and 760 nm (R² = 0.59) based on the experiment data for winter wheat, which were also validated by three independent experiments, and the validation results showed that the LUEChlF relation was weakened, possibly by different species, canopy density and environmental conditions. Furthermore, the significant negative relation between non-photochemical quenching (NPQ) and PRI was confirmed. However, the PRI-LUE relation was evidently weakened by the canopy and environmental conditions. The PRI difference (ΔPRI) from the minimum reference PRI around noontime could greatly eliminate the interference factors. The LUE-ΔPRI model was developed based on the experiment data for winter wheat (with an R² value of 0.85), and validated by other three independent experiments.

Citation Download Citation

Liangyun Liu, Quanjun Jiao, and Dailiang Peng "Exploring vegetation photosynthetic light-use efficiency using hyperspectral data", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85311C (19 October 2012); https://doi.org/10.1117/12.971189

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