# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 5 Conclusions  
We presented a web application for generating fine-spatial-resolution GPP driven by 30-m Landsat time-series data (i.e., \(\mathrm{GPP_{L}}\)). The \(\mathrm{GPP_{L}}\) precisely characterized the spatial heterogeneity and the seasonal variations in the GPP of complex landscapes, while the coarse-spatial-resolution remote sensing data could not reproduce these conditions. Significant improvements in \(\mathrm{GPP_{L}}\) were found in agroecosystems and dryland ecosystems, such as shrubland and savanna, compared to the coarse-spatial-resolution GPP products.  
The EC-LUE model driven by fine spatial resolution with optimized coefficients also corrected the GPP estimation biases for the cropland and dryland, which could be used to reproduce the total amount of plant photosynthesis. Furthermore, the acquisition of the Landsat data strongly affected the estimation accuracy of the GPPL. Therefore, future work should apply spatio-temporally continuous reconstructed fine-spatial-resolution remote sensing data to the GPP product. Furthermore, since the land-cover map on the GEE platform changes every five years, future work on GPP mapping should apply the dynamic land-cover map, which can capture the GPP changes caused by annual land cover changes. With our online APP on GEE, users can generate GPP at any given location and for any year since 1984 on demand. This will help with the development of further applications that investigate the relationship between GPP and other parameters.  
Conceptualization, S.L. and W.Y.; methodology, X.H., Y.Z. and X.Z.; software, S.L. and X.Z.; validation, S.L. and X.H.; resources, Y.Z.; writing--original draft preparation, S.L. and W.Y.; writing--review and editing, S.L., Y.Z. and W.Y.; visualization, S.L.; funding acquisition, S.L. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number No. 42141020, National Natural Science Foundation of China, grant number No. 42101319, China Postdoctoral Science Foundation Funded Project, grant number 2021M/03658, and the Fundamental Research Funds for the Central Universities, Sun Yet-sen University (2021 qntd29). The APC was funded by China Postdoctoral Science Foundation Funded Project (2021M/03658).  
This work used eddy covariance data acquired and shared by the FLUXNET community, including the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeanP, Corboltaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, Swiss FluxNet, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and the Fluxneta project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux, and AsiaFlux offices. This work was also supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I), grant number LO1415 and by the project for national infrastructure support CzeCOS/ICOS, reg. no. LM2015061.  
The authors declare no conflict of interest.