# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 1 Introduction  
Vegetation gross primary production (GPP), as the principal component of terrestrial ecosystems, plays an important role in supporting global sustenance and energy cycles, regulating the carbon balance and alleviating the rise in atmospheric CO\({}_{2}\) concentrations and global climate change. However, the modeling of GPP is one of the largest sources of uncertainty in global carbon cycle [1, 2]. The current satellite-derived GPP products are based on coarse-spatial-resolution data (500 m-8 km), which cannot reproduce the spatial heterogeneity of GPP and leads to uncertainties in GPP estimates [3]. Previous studies showed that the spatial heterogeneity in farmland led to a 30% underestimation in GPP from the agroecosystem [4], while [PERSON] et al. (2010) showed a 5% overestimation in forest ecosystems [5]. Therefore, the improved capturing of the spatial heterogeneity of landscapes can reduce GPP simulation uncertainty. With fine-spatial-resolution remote sensing data as the input, light use efficiency (LUE)-based GPP models, which account for both physiological process and vegetation structure change, can precisely characterize the GPP in heterogeneous regions [6, 7]. Studies showed that the GPP derived from fine-spatial-resolution satellite-based datasets has a better performance than coarse-resolution GPP products because fine-resolution satellite data can capture the heterogeneity of vegetation cover [6]. For example, a LUE model driven by fine-spatial-resolution remote sensing data can reduce GPP simulation uncertainties by20% [4]. [PERSON] et al. (2012) showed that fine-spatial-resolution GPP driven by a LUE model captured the spatial and temporal changes in GPP in a typical agroecosystem [8]. Past researches focused on how fine-spatial-resolution data improved GPP estimation [9; 10], but the lack of multi-site studies prevented the assessment of how spatial heterogeneity influenced GPP modeling globally. More importantly, the collection of fine-scale remotely sensed data requires a significant amount of storage space, and computing fine-spatial-resolution GPP is time-consuming. For example, the Landsat 8 project acquired around 500,000 images at a 30-meter spatial resolution per year, requiring an enormous data storage capacity (\(\sim\)600 TB) [11]. Before users can execute a study, a large amount of time is spent accessing the data and completing the necessary preprocessing steps. Thus, it is difficult to download such a significant amount of data to derive fine-spatial-resolution GPP for various sites. Using the Google Earth Engine (GEE) platform to generate such fine-spatial-resolution GPP products on demand can save significant time and data storage. Furthermore, the revisit periods of fine-spatial-resolution-satellite remote sensing data (for example, \(\sim\)16 days for Landsat, \(\sim\)5 days for Sentinel-2), are much longer than those of coarse spatial resolution, which can provide almost two observations each day in the case of moderate-resolution-imaging spectro-radiometer (MODIS) and advanced very-high-resolution radiometer (AVHRR) series. Therefore, the availability of fine spatial remote sensing data may fail to represent the growing condition of vegetation and lead to high uncertainties in GPP modeling. However, few studies have focused on this topic. In this study, we used the Eddy Covariance-Light Use Efficiency (EC-LUE) model [12] and long-time-series Landsat data as the input to derive fine-spatial-resolution GPP and compared it to the GPP driven by coarse-spatial-resolution remote sensing data. We asked the following questions: (1) How and where did the fine-spatial-resolution remote sensing data improve the GPP estimation compared to coarse-spatial-resolution GPP? (2) How did the data missing from Landsat affect GPP modeling accuracy? To address these questions, we built a web application (APP) on GEE to generate GPP with Landsat 30-meter-spatial-resolution data and compared the fine spatial resolution GPP with the existing coarse-spatial-resolution GPP products.