# GPP of a Chinese Savanna Ecosystem during Different
## 1 Introduction
Globally, phenology-based seasonal gross carbon uptake by terrestrial ecosystems exhibits asymmetric spatiotemporal dynamics. Phenology-based definitions of seasonality might offer a better starting point for studying spatiotemporal patterns of seasonal GPP dynamics than definitions of meteorological seasons do [35]. Hence, considering canopy phenophase information in the GPP estimation process could improve accuracy [36]. Limited research has been conducted in tropical water- or light-limited/driven environments, in contrast to studies of temperature-driven trends in vegetation phenology. Grasslands and trees in savannas exhibit different phenological strategies, and such strategies directly affect the relative contributions of grasses and trees to net primary production [37]. By analyzing data from 3 EC towers in a Mediterranean-type savanna, ref. [38] reported that phenology affects the uncertainty in carbon flux estimates and that spatial heterogeneity significantly contributes to differences in savanna flux estimates. Moreover, ref. [21] identified that the scarcity of valid Landsat data for Brazilian forest vegetation limited phenological monitoring and suggested that the combination of Landsat data with Sentinel-2 data should be enhanced in future studies. Studying the GPP in savannas during different phenophases within the context of global vegetation phenological shifts and increased interannual variability in climatic drivers due to global climate change is particularly important [2]. It has been shown that LUE models perform differently in modeling the GPP at different phenological stages [16; 39], but the types of remote sensing-based savanna GPP changes during different phenophases using the VPM remain uncertain, especially when combined with high-resolution remote sensing data. It is essential to couple technological advances with scientific questions in the study of phenology and savanna GPP [40]. In this study, phenocam images were combined with EC and satellite data to provide insights into the dynamics of phenological processes and their relationships with VPM GPP estimates for a typical Chinese savanna ecosystem. The main objective of this research was to simulate the GPP in this savanna and evaluate the modeling results via in situ data from our flux tower station in Yuanjiang, Yunnan, China. Furthermore, since HLS data have recently been proposed as very promising remote sensing data and since there are no readily available HLS data products that cover the study period, we aimed to harmonize Landsat and Sentinel-2 data by leveraging the advantages of the built-in functions, coherent data structures, and computational power of the GEE. We extracted essential variables as key VPM inputs by exploiting the GEE capabilities via remote sensing HLS data and estimated the GPP in the selected savanna via the VPM. We aimed to answer the following questions: (1) Is the VPM suitable for simulating the GPP in savamas in China? (2) How does the VPM perform using high-resolution HLS data? (3) How does the estimated GPP in Chinese savannas behave during different phenophases?