A novel SIF framework for decoupling hierarchical water stress impacts on winter wheat photosynthesis

Abstract

Drought has emerged as a critical constraint on sustainable agricultural development, particularly in water-scarce agroecosystems where multiscale hydrological stresses—such as rainfall deficits, soil moisture depletion, and groundwater exhaustion—interact to undermine crop productivity stability. However, current remote sensing frameworks lack the capacity to isolate and quantify the independent impacts of groundwater drought on crop photosynthesis and yield, leading to long-standing underestimation of deep-layer hydrological stress, especially in groundwater-dependent regions. To address this gap, we have proposed a dynamic monitoring framework based on solar-induced chlorophyll fluorescence (SIF) to assess the photosynthetic response characteristics of winter wheat across the Huang-Huai-Hai Plain under precipitation, surface moisture, and groundwater anomalies. This framework integrates dynamic time warping (DTW) for interannual phenological alignment and constructs a quantile-based dynamic baseline library that overcomes the limitations of traditional normality-based anomaly metrics. On this basis, we have developed a novel photosynthetic response anomaly index (PRAI) to characterize the spatiotemporal evolution of drought-induced photosynthetic stress. Results reveal that groundwater anomalies induce a significantly lagged crop response (mean lag ≈ +2.1 months, p < 0.01) and exert stronger influence on photosynthetic dynamics than soil surface moisture or rainfall. PRAI exhibits more concentrated and persistent negative anomalies during groundwater drought years, correlating more strongly with yield loss (R² = 0.53) than during meteorological drought years (R² = 0.30). Cross-validation using evapotranspiration (ET) and vegetation optical depth (VOD) further confirms PRAI reliability in capturing physiological stress. The proposed SIF-based dynamic monitoring framework not only deepens the understanding of crop eco-physiological response mechanisms to multiscale water stress, but also provides critical scientific support and methodological innovations for regional scale precision agriculture, crop model optimization, and sustainable water resource management.