IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2024)

Exploring Physiological and Nonphysiological Responses of Sun-Induced Chlorophyll Fluorescence to Different Levels of Water Stress in Winter Wheat

  • Jingyu Lin,
  • Litao Zhou,
  • Jianjun Wu

DOI
https://doi.org/10.1109/JSTARS.2024.3363140
Journal volume & issue
Vol. 17
pp. 5107 – 5120

Abstract

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Sun-induced chlorophyll fluorescence (SIF) has recently been used for the early detection of plant stress. The response of SIF to water stress is complicated by the combination of physiological and nonphysiological dynamics in SIF variations and has not been well explored. This article aims to explore the physiological and nonphysiological responses of SIF under different levels of water stress, by analyzing the data from winter wheat with four irrigation treatments (well-watered, moderate, severe, and extreme water stresses). Here, the near-infrared radiance of vegetation (NIRvR) and the canopy near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) were selected to represent the nonphysiological information of SIF. Then, the physiological information (fluorescence yield, ΦF) was extracted by dividing SIF by NIRvP or NIRvR. The results indicate that the physiological and nonphysiological components of SIF were effective indicators for water stress detection. Compared to NDVI, the physiological and nonphysiological components exhibited faster responses to different levels of water stress. The nonphysiological component explored 68%–77% of SIF under different levels of water stress, and its contributions were first decreased and then increased as water stress increased. The stress-induced physiological decrease explored ∼50% of SIF decrease under severe water stress and exceeded the nonphysiological decrease at the onset of extreme water stress. This study provides direct insights into the physiological and nonphysiological dynamics of SIF under different levels of water stress, which contribute to improving the detection of plant stress using SIF.

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