## 1 Introduction  
Vegetation phenology, the study of the periodic biological life cycle events of plants, is a critical regulator of carbon and water cycling in terrestrial ecosystems [1]. The trend of global warming has aroused great interest in understanding and monitoring the dynamics of vegetation phenology under the changing climate [2]. As a valuable indicator of climate variability and ecosystem responses [1, 3], accurate measurement of land surface phenology (LSP) is crucial for better explicating the land-atmosphere-energy exchange and its representation in terrestrial biosphere models [4, 5, 6, 7]. Phenological studies are usually conducted at the species- or ecosystem-level through ground-based field techniques [4], such as visual inspection, eddy covariance flux towers, near-surface spectral radiometers, and digital cameras [8]. Satellite-based observation, providing a unique and feasible way for examining broader-scale phenomena [4], has notably expanded the horizon of traditional phenology observation [2]. Vegetation indices, which are combinations of surface reflectance at two or more wavelengths designed to highlight a particular property of vegetation [9; 10; 11], can depict spatiotemporal patterns of the timing of plant growth, senescence, and dormancy at seasonal and inter-annual time scales [12]. The enhanced vegetation index [9], normalized difference vegetation index [13], and leaf area index (LAI) derived from optical satellite sensors are widely applied for tracking large-scale vegetation seasonality. Dryland vegetation, covering approximately 40% of the global land surface [14], is exceedingly susceptible to climate conditions, mainly owing to chronic physiological stress [10; 15; 16; 17]. Numerous phenology studies based on satellite remote sensing have examined temperature-driven ecosystems of the Northern Hemisphere [4; 18; 19]; however, the phenological dynamics of dryland vegetation under climate variability and extreme events have not been thoroughly investigated as a result of the intrinsic sensitivity and complexity [15]. Given that the mixed and heterogeneous arrangement of water-limited ecosystems confounds the extraction of the phenophase, [PERSON] et al. (2014) assessed dryland vegetation phenology across an elevation gradient in Arizona, USA, by fusing synthetic Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery [15]. The significant spatiotemporal variations in savanna biomes with diverse tree-grass ratios implies that grass-containing seric savanna is especially sensitive and vulnerable to hydro-climatic variability [20]. Besides, previous studies found that growing season retrieved from the greenness-based approach overestimates the photosynthetically active duration, indicating a divergence between greenness and function [21; 22]. On the basis of the complex composition of plant functional types, as well as the highly dynamic climate in Australia, accurately assessing the phenological characteristics of dryland vegetation remains challenging [23]. In contrast to the reflectance-based approaches, fluorescence measurements directly from the core of photosynthetic machinery present a fresh manner of remotely sensing vegetation growth and response. Recent studies found that satellite-based observations of SIF can detect interannual and seasonal variations in gross primary production of vegetation in North America [24; 25; 26]. [PERSON] et al. (2018) found strong spatiotemporal consistency between satellite-based SIF and GPP products across the Contiguous United States [27]. Although photosynthesis and greenness exhibit an analogous seasonality in deciduous forests, satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests [28; 29]. Continuous observations of satellite-based SIF and vegetation indices contribute to improved understanding of large-scale variations in vegetation phenology and function [21].