# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## Results
### Assessment of the Variations in the Merged GPP  
GLASS GPP is a dataset generated by the light use efficiency model integrating CO2, radiation, and vapor pressure deficit (VPD), providing a reliable long-term GPP estimation. Figure 6 shows the long-term changes in GLASS GPP, CMIP6\({}_{\text{DL}}\) GPP, and multi-model ensemble mean from 1982 to 2014. As shown in Figure 6a, the variations in GLASS GPP are not apparent and almost remain unchanged in the northwest arid regions. There is an increasing trend in Central China and a significant decrease in the eastern part of Inner Mongolia, the southern part of East China, and South China. The long-term changes in the multi-model ensemble mean are shown in Figure 6c. There is a sharp increasing trend throughout China, except for the western regions. The characteristics of the decrease in the southern coastal areas and Inner Mongolia are not captured, which is inconsistent with the spatial characteristics of the variations in GPP over China. Figure 6b describes the spatial distribution of the long-term variations of the merged GPP data, similar to GLASS GPP. However, the variations are gentler than the reference data since the deep learning merging model is built using a convolution neural network with a \"smoothing\" effect. That is, it is impossible for the deep learning merging model to learn the extreme value. Compared with the multi-model ensemble mean, the data quality has been significantly improved in eastern Inner Mongolia and South China, dramatically increasing the merged dataset's reliability in the projection period.  
To evaluate the differences in the merged results in the time dimension, the grids are randomly selected in four climatic regions over China (arid, semi-arid and semi-humid, humid region, and Qinghai-Tibet Plateau) to test the time series. As can be seen from Figure 7, there are similar seasonal fluctuations in the three datasets. The fluctuation range of the test dataset is highly consistent in the CMIP6\({}_{\text{DL}}\) GPP and the reference data, while it is more significant in the multi-model ensemble mean. GPP has consistently been overestimated throughout the period in the multi-model ensemble mean. As the GPP increases, the error of the multi-model ensemble mean increases. Specifically, the high value of GPP is concentrated in June and July, when the error of the multi-model ensemble mean is the largest. The impacts of temperature, water, solar radiation, and vegetation growth are the main causes of seasonal variation. The temperature, precipitation, vegetation leaf area, and solar radiation of each region reach the highest values during in the year in summer, and then gradually decrease. The comprehensive impact of these factors ultimately leads to an inverted U-shaped change in the average monthly GPP of each vegetation coverage area.  
Figure 6: Long-term variation trends of (**a**) GLASS GPP, (**b**) CMIP6\({}_{\text{DL}}\) GPP, and (**c**) multi-model ensemble mean from 1982 to 2014, with the dot indicating passing significance test at 5% level.