IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2021)

Effects of Drought on the Relationship Between Photosynthesis and Chlorophyll Fluorescence for Maize

  • Jidai Chen,
  • Xinjie Liu,
  • ShanShan Du,
  • Yan Ma,
  • Liangyun Liu

DOI
https://doi.org/10.1109/JSTARS.2021.3123111
Journal volume & issue
Vol. 14
pp. 11148 – 11161

Abstract

Read online

Structural–physiological factors affect the accurate estimation of vegetation gross primary production (GPP) under various types of environmental stress. Solar-induced chlorophyll fluorescence (SIF), which is directly linked to photosynthesis, has been effectively used to estimate and monitor GPP. However, understanding of the physiological mechanism linking SIF to GPP under stress remains limited. In this article, the link between SIF and GPP at diurnal and seasonal timescales was explored for a maize field and its response to drought stress (as defined by the crop water stress index, CWSI) using three-years of continuous tower-based measurements was investigated. The results show that the ratio of GPP to SIF decreased with increasing drought stress levels, and the canopy stomata conductance (Gs) declined synchronously. Compared to two canopy structural factors (NDVI and NIRv), both the Pearson and partial correlation coefficients for the relationship between Gs and the ratio of GPP to the total SIF was higher (0.38, p < 0.01 and 0.32, p < 0.01 with photosynthetically active radiation as the control variable, respectively). We also found that ${{\bf \Phi }_F}$ tracked the changes in LUE well under drought conditions (CWSI > 0.6), which demonstrated that SIF can be a powerful parameter for estimating GPP under drought stress. However, there was a smaller drop in ${{\bf \Phi }_F}$ under drought stress (slope = –0.002) compared to the slope for the relationship between LUE and CWSI (–0.08). The response of light reactions to drought stress may be muted compared to the stomatal response. These findings confirm that the Gs is sensitive to drought and is important for the SIF-based GPP estimation model. It also provides reliable evidence that SIF data include a large amount of physiological information and can serve as a potential indicator for detecting drought and estimating GPP.

Keywords