Probabilistic approach to monitoring vegetation water stress using solar-induced chlorophyll fluorescence data

Abstract

Solar-induced chlorophyll fluorescence (SIF) provides valuable insights into plant stress by detecting reductions in photosynthesis that frequently occur during drought. Unlike climate-based drought indices, SIF directly measures the photosynthetic activity and vitality of vegetation, providing a unique and real-time perspective for examining the effects of water stress. The vegetation water stress index (SIF-Di) is calculated using a probabilistic method, and a meteorological composite drought index (CDI) is employed to monitor vegetation health and drought conditions. The probabilistic approach categorizes monthly SIF anomalies based on percentiles, with lower percentiles indicating more severe vegetation water stress. A dynamic time warping approach is employed to investigate how SIF responds to climatic drought. The SIF-Di captures vegetation water stress activity well across global river basins. The results revealed that the Amazon basin has a CDI that leads the SIF-Di by 5.94 ± 6.24 lag times, suggesting that vegetation water stress develops gradually due to the dense rainforest canopy, as deep-rooted vegetation allows plants to tap into subsurface water, which increases resiliency and delays stress during prolonged dry periods. The SIF-Di and CDI offer a new approach to drought intensity, particularly in basins where climate drought affects vegetation with a relatively small lag. For example, the Mackenzie and Danube basins, with lags of 0.68 ± 1.63 and 0.84 ± 1.89 months, respectively, are vulnerable to drought and act as models for estimating drought response mechanisms. This study could enhance the predictability of drought onset and severity by anticipating the time difference between vegetation water stress and climatic drought.