Detection of fast-changing intra-seasonal vegetation dynamics of drylands using solar-induced chlorophyll fluorescence (SIF)

作者：Jiaming Wen, Giulia Tagliabue, Micol Rossini, Francesco Fava, Cinzia Panigada, Lutz Merbold, Sonja Leitner, Ying Sun · 发表于：Biogeosciences · 年份：2025 · DOI：10.5194/bg-22-2049-2025 · 被引用次数：5 · 研究领域：Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics、Land Use and Ecosystem Services

Abstract. Dryland ecosystems are the habitat supporting 2 billion people on Earth, and they strongly impact the global terrestrial carbon sink. Vegetation growth in drylands is mainly controlled by water availability with strong intra-seasonal variability. Timely availability of information at such scales (e.g., from days to weeks) is essential for early warning of potential catastrophic impacts of emerging climate extremes on crops and natural vegetation. However, the large-scale monitoring of intra-seasonal vegetation dynamics has been very challenging for drylands. Satellite solar-induced chlorophyll fluorescence (SIF) has emerged as a promising tool to characterize the spatiotemporal dynamics of photosynthetic carbon uptake and has the potential to detect intra-seasonal vegetation growth dynamics. However, few studies have evaluated its capability of detecting fast-changing intra-seasonal vegetation dynamics and advantages over traditional approaches in drylands based on vegetation indices (VIs). To fill this knowledge gap, this study utilized the vast dryland ecosystems in the Horn of Africa (HoA) as a testbed to characterize their intra-seasonal dynamics inferred from satellite SIF. The HoA is an ideal testbed because its dryland ecosystems have highly dynamic responses to short-term environmental changes. The satellite-data-based analysis was corroborated with a unique in situ SIF dataset collected in Kenya – so far, the only ground SIF time series collected on the con...