The Afternoon/Morning Ratio of Tower-Based Solar-Induced Chlorophyll Fluorescence Can Be Used to Monitor Drought in a Chinese Cork Oak Plantation

Monitoring drought stress is crucial for estimating productivity and assessing the health status of forest ecosystems under global climate change. Solar-induced chlorophyll fluorescence (SIF) is mechanistically coupled to photosynthesis and has advantages over greenness-based vegetation indices in detecting drought. In recent years, SIF has commonly been used in monitoring drought stress in crop ecosystems. However, the response of tower-based SIF to drought stress in forest ecosystems remains unclear. In this study, we investigated the potential of tower-based SIF to monitor drought, which was quantified using the plant water stress index (PWSI) in a Chinese cork oak plantation. The results show the negative effect of drought on SIF, and afternoon depression of SIF emission under drought stress was observed. Canopy SIF (F) exhibited a nonlinear relationship with PWSI, while the quantum yield of SIF (ΦF) exhibited a significant linear relationship with PWSI at 687 nm and 760 nm (ΦF687: R2 = 0.90; ΦF760: R2 = 0.85). Incident radiation (PAR) and canopy structure affected the response of SIF to drought stress, with PAR as the main factor causing the nonlinear relationship between F and PWSI. Afternoon depression was described as the afternoon/morning ratio (AMR). AMRF and AMRΦF exhibited a negative linear response to PWSI. AMRF was less affected than F by PAR and canopy structures, and AMRΦF was more physiologically representative than ΦF. Moreover, AMRΦF was sensitive to VPD and REW, and it might be a good indicator of drought. Red SIF was more sensitive to drought than far-red SIF, as the R2 of PWSI with AMRΦF687 (R2 = 0.89) was higher than that with AMRΦF687 (R2 = 0.84). These results highlight the potential of tower-based SIF, especially red SIF, for drought monitoring in a plantation, and consideration of the physiological diurnal variation in SIF under drought stress is crucial for improving the accuracy of drought stress monitoring.