# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 4 Discussion
[5] compared GPP simulation differences in North America across 26 models to explore the responses of different model structures and environmental factors to simulations based on data from 39 flux stations. [PERSON] et al. [78] studied the impact of different parameter settings and meteorological data in the CEVSA2 model on the GPP simulation results in the Changbai Mountain region of China from 2003 to 2005, indicating that the GPP difference caused by parameter setting in the model is 5% to 8%. The reference data GLASS GPP is generated by integrating multiple algorithms using the Bayesian integration method, reducing the uncertainty of a single  
Figure 12: Inter-annual variations in CMIP6\({}_{\mathrm{DL}}\) GPP at baseline (1995–2014) and in the future (2015–2100) under climate scenarios over China. algorithm. However, there are differences in spatial scales between ground and satellite data, and the algorithm fails to distinguish potential differences in photosynthesis between C3 and C4 crops. In addition, the problem of mixed pixels can also affect the accuracy of GLASS GPP estimation [79]. The GPP observed by the flux tower is considered the benchmark, while the high heterogeneity of the ground can also make the representativeness of the flux station data poor. Therefore, significant uncertainties in the GPP benchmark lead to a lack of consensus on the global GPP distribution [15; 80]. [PERSON] et al. [81] showed that the flux-tower-estimated GPP represents 50% to 60% of the actual situation in the case of surface heterogeneity. There is still a problem of insufficient spatial representation compared to the range of flux contribution areas assumed by eddy covariance. The mismatch between the spatial resolution of the model and the floor area of the flux tower causes the uncertainty imposed by scale effects. Even with the same model and source of input data, the simulation results may still be different due to spatial heterogeneity caused by different spatial resolutions [1]. At the regional scale, improving spatial resolution can reduce the uncertainty of GPP simulation [82]. However, in practical applications, models often need to simulate large-scale and long-time series data. When considering factors such as simulation time, data storage capacity, and analysis difficulty, they often reduce the spatial resolution to improve simulation efficiency. In GPP estimation at different spatial scales, there will be different judgments on the coverage type, leading to incorrect maximum light energy utilization, further resulting in errors. In mid- and low-resolution GPP remote sensing products, most of the pixels under heterogeneous ground surfaces are mixed. For example, there is a significant difference between MODIS surface coverage with 1 km and global 30 m resolution. At different scales, heterogeneity characteristics have an important impact on surface vegetation parameter characteristics [83]. Taking the mixed pixel of forest and grassland as an example, there is a significant difference in the a priori maximum light energy utilization rate between the two types. It may bring about significant errors using a single type of maximum weak energy utilization rate to replace pixel values. [PERSON] et al. [1] discussed the impact of two spatial resolutions of 1 km and 8 km input data on GPP simulation, with the results showing that the difference was mainly due to the difference in LAI caused by mixed pixels within the range of 8 km. The difference in simulation results with different spatial resolutions at forest stations was more significant than that at grassland stations. Therefore, it is feasible to appropriately reduce spatial resolution to improve the model's simulation efficiency. However, it is necessary to minimize the error of low spatial resolution in the GPP simulation of forest ecosystems and growing seasons.