# GPP of a Chinese Savanna Ecosystem during Different
## 1 Introduction
High-temporal-resolution products (e.g., Moderate-Resolution Imaging Spectrometer (MODIS) and Medium-Resolution Imaging Spectrometer (MERIS) products) are generally too coarse (from 250 m to a few kilometers) to capture tropical ecosystem heterogeneity [20]. Moreover, owing to their long revisit time (16 days), medium-spatial-resolution products (e.g., Landsat products with a 30 m spatial resolution) potentially lack observations at critical phenological stages [21]. The combination of data from different ongoing satellite missions, such as Landsat-7/8 and Sentinel-2, provides an unprecedented opportunity to estimate the GPP at the regional scale [22]. However, harmonizing L7, L8, and S2 data remains a challenging process that requires several procedures. Whether HLS data can be used to simulate the GPP in savannas is unknown because the required procedures are data-demanding and computationally intensive [23]. The Google Earth Engine (GEE) has significantly streamlined data management and accelerated the analysis process by providing access to planetary-scale archives of remote sensing data, including Landsat7, Landsat-8, and Sentinel-2 data. The inherent functions and algorithms embedded within the GEE platform involve numerous preprocessing tasks, enabling users to focus on interpreting fundamental algorithms [24]. With the GEE platform, there is a new opportunity for savanna GPP simulation. Much information on the response of savannas to climate change can be obtained from monitoring savanna phenology [25]. Studying the relationship between phenology and GPP changes can enhance our understanding of the functions and characteristics of diverse vegetation ecosystems worldwide [26]. To better understand seasonal patterns and the response of forest canopies to interannual and long-term changes in climate, various approaches have been adopted to obtain temporal canopy information. Satellite remote sensing techniques have been widely applied in vegetation phenology research to detect the timing of phenological events in greenness-related vegetation indices [27]. However, savannas constitute complex assemblages of tree-shrub-grass layers, each with distinct phenological responses to seasonal climate and environmental variables, and accurate and robust retrievals of savanna phenology information via remote sensing methods remain a challenge [28]. Recently, automatically captured digital camera images, a form of near-surface remote sensing, have been reported as a promising new tool for accurately monitoring vegetation phenology at the ecosystem scale [29]. A digital camera (also referred to as a phenocam) can continuously provide imagery over time and is robust to variations in illumination conditions. Phenocams are rarely obscured by clouds and can collect high-temporal-resolution data with lower operating costs than can remote sensing satellites [30]. Digital repeat photos provide quantitative values of color indices, which allows researchers to detect changing phenological patterns. Moreover, phenocams are widely used at CO\({}_{2}\) flux tower monitoring sites to interpret the seasonal variability in GPP [31]. The color indices derived from photos are correlated with the tower flux-based GPP and can be used to estimate the spatial and temporal distributions of photosynthetic productivity [25]. Research on productivity patterns and drivers in savanna is urgently needed because it is central to understanding the response of savannas to climate change [32]. Further evaluation of VPM simulations with phenological analysis for other savannas is necessary before this model can be applied to estimate the GPP in savanna ecosystems at the continental scale [33]. Recently, it has been recognized that leaf phenology is an important driver of canopy gas exchange, water use efficiency, and photosynthetic capacity changes [34]. Vegetation phenology is an important indicator that is closely connected to changes in forest ecosystem productivity.