# GPP of a Chinese Savanna Ecosystem during Different
## 1 Introduction  
One-third of terrestrial net primary production is stored in savannas annually, which constitute an important component of the overall land carbon sink [1]. The climate and photosynthesis in savannas are characterized by distinct wet and dry seasons. Climate change is predicted to profoundly affect savanna ecosystems in terms of productivity and light-water use efficiency [2]. As reported in previous studies [3, 4], savannas playan important role in the cycle of several greenhouse gases and in energy fluxes (latent and sensible heat fluxes) within the context of climate change. Investigating the distinct responses of carbon and water fluxes to environmental factors in savannas is critical [5]. The gross primary production (GPP) dynamics in Chinese savanna ecosystems and in other regions worldwide have also been studied because of the need to better understand the effects of climate change on regional and global biogeochemical cycles [2; 6]. Notably, this approach allows us to estimate the contributions of savannas to regional and global climate controls, including the relationships among climatic factors, plant productivity, phenological feedback, and greenhouse gas exchange within the atmosphere [7]. Compared with areas such as Africa and Australia [8], where savannas are more widely distributed, the savanna area in China is relatively small and sparse. Yunnan Province, Southwest China, has a monsoonal climate suitable for savannas and encompasses vast areas with savanna physiognomy. However, the savanna ecosystem in Yunnan Province also faces fragmentation and is in urgent need of protection [9]. Our research focused on formally recognized savannas in the Yuanjiang dry valleys of Yunnan Province in Southwest China. The Yuanjiang savanna is the most typical savanna ecosystem in China [10] and is the only savanna ecosystem site among the multiple existing carbon flux observatories in China. Both eddy covariance (EC) and remote sensing data are extremely important for providing reliable information on CO\({}_{2}\) exchange in savannas where the flux tower coverage is limited or nonexistent. In traditional remote sensing-based GPP simulation methods, the GPP is calculated via light use efficiency (LUE) models that are based on vegetation indices (such as the enhanced vegetation index (EVI)) and meteorological data (such as solar radiation and temperature) as inputs. Numerous LUE models have been developed with different combinations of vegetation indices and expressions for environmental stress [11]. In several studies, GPP data derived from LUE models have been compared with GPP EC data obtained from EC flux tower sites [12; 13]. However, savanna vegetation comprises both C3 and C4 plants, of which the GPP is more difficult to simulate and study than that in ecosystems of a single plant type (C3 or C4 composition). Among these LUE models, the satellite-based vegetation photosynthesis model (VPM) was the first to consider the fraction of photosynthetically active radiation (PAR) absorbed by vegetation chlorophyll (FPAR\({}_{\text{chl}}\)) to estimate the GPP [14]. The VPM is frequently used for GPP estimation because of its clear structure and simple parameters. Recently, this model has been further developed and applied to various ecosystems [15], including forests [16], grasslands [17], and savannas [18]. With the use of the VPM, ref. [15] estimated the GPP of different vegetation types globally, including savannas, by setting different photosynthetic use efficiency values for C3 and C4 plants. Geographically, the VPM has been employed to simulate the GPP in Mediterranean-type savannas, savannas in North America, and the cerrado in South America [18; 19]. However, the satellite data used in the VPM are typically Moderate-Resolution Imaging Spectroradiometer (MODIS) remote sensing data, whereas VPM simulations with high-resolution data, such as Sentinel-2 and harmonized Landsat and Sentinel-2 (HLS) data, are rare.