## 1 Introduction  
Wetlands only cover 3%-8% of the Earth's land surface, but store 15%-30% of the world's terrestrial soil carbon [1; 2], indicating that they play an important role in the global carbon (C) cycle and potentially have a significant impact on global climate change [3; 4; 5]. Vegetation phenology is a fundamental determinant affecting the processes of carbon, water, and energy exchange in wetland ecosystems [6; 7]. Phenology determines the timing and duration of the photosynthetically active canopy and drives annual carbon uptake in wetland ecosystems [8; 9; 10]. However, changes in the global and regional climate, such as rising global mean temperatures and changing precipitation regimes [11; 12; 13], have significantly affected wetland vegetation dynamics and carbon dynamics. Warming may stimulate plant phenological development, increase nutrient mineralization rates, and lengthen the growing season, which in turn may increase plant primary production and carbon sequestration [14; 15]. Furthermore, warming can accelerate biospheric metabolism, resulting in a greater release of greenhouse gases to the atmosphere, which in turn reinforces anthropogenic warming [3; 16]. Therefore, quantifying the relationships among climate change, phenological variability, and vegetation production is crucial for investigating accurate C budgets in alpine wetland ecosystems, especially in the context of global warming. The largest highland wetland in the world, the Zoige alpine wetland, is situated at the eastern edge of the Qinghai-Tibetan Plateau. Because of the high altitude, this wetland is highly sensitive to climatic changes [14; 17; 18]. Continuously rising temperatures and declining precipitation characterize climate change in this region [4; 5]. These changes may have decreased soil moisture and accelerated thawing of permafrost, causing wetland degradation and potentially increasing CO\({}_{2}\) efflux to the atmosphere. Climatic changes may also influence plant phenology, _i.e._, the timing of flowering and other developmental stages. Shifts in plant phenology can affect the carbon cycle, water cycle, and energy fluxes through photosynthesis and evapotranspiration, and potentially increase CO\({}_{2}\) influx to the wetland ecosystem. Moreover, the changes in local climate may produce a positive feedback, mediated by the greenhouse effect from increased CO\({}_{2}\) emissions [5]. Therefore, to improve our understanding of C balance dynamics in the Zoige alpine wetland, the effects of warming and phenological variability should be studied simultaneously. Over the past few decades, development of the eddy covariance (EC) method and satellite-based models have greatly increased opportunities for exploring the potential effects of climate change and vegetation phenology on C exchange between terrestrial ecosystems and the atmosphere. The EC method can provide continuous measurements of ecosystem-level exchanges of carbon, water, and energy fluxes [19; 20], but such measurements have only been made at several research sites and often over short time periods [21]. Remote sensing (RS) technology is an important tool in the characterization of vegetation phenology [22; 23] and estimation of gross primary production (GPP) [24]. It has become an effective tool to estimate either GPP or net primary production (NPP) at regional scales, which can be compatible with the EC flux tower footprint [25; 26]. Recently, a satellite-based, vegetation photosynthesis model (VPM) developed by [PERSON] _et al._ simulated GPP based on the conceptual partitioning of chlorophyll and non-photosynthetically active vegetation within the canopy. The VPM model has been further developed and has the potential to scale up _in situ_ observations of GPP from EC flux tower sites across ecosystems [25; 27; 28; 29]. However, performance of the VPM model in simulating the long-term GPP of alpine wetland ecosystems and application to investigation of the relationships among wetland phenology, vegetation production, and climate change is still unknown.