# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 4 Conclusions  
The use of ODC grids for the storage and application of satellite imagery data is a fresh attempt for the exploration of satellite imagery data organization and management in the era of big data, which provides new approaches to this field. However, the current remote sensing data infrastructure in China cannot meet the actual requirements of remote sensing applications in terms of capacity, scalability, usability, and performance. The integration, management, and on-demand service capacity of remote sensing big data infrastructure have become the bottleneck of remote sensing science and engineering in China [50]. Therefore, a Chinese GF-1 satellite imagery data management framework based on ODC grids was implemented, which extended the ODC grid in the loading and processing support for Chinese GF-1 satellite imagery data. To verify the reliability of the proposed framework, we conducted water and vegetation monitoring experiments on the Miyun Reservoir in Beijing with multiyear GF-1 satellite imagery data. Compared with  
Figure 8: Spatial distribution of NDVI and NDWI in and around the Miyun Reservoir from 2014 to 2021.  
the traditional scene-based data management approach, the ODC grid-based framework significantly improved the temporal efficiency on average by 51.8% and spatial efficiency on average by 48.5% in our experiments, thus demonstrating the advantages and potential of the ODC grid as a new data management paradigm. The proposed framework focused only on the loading and application analysis of the Chinese GF-1 satellite imagery data, and the management of other types of Chinese satellite imagery data was not considered. Therefore, the proposed framework has great room for expansion in terms of remote sensing data types, and future work may focus on the loading of more data types (including the HJ imagery data, the GF series imagery data, and the JL1 imagery data). Secondly, the proposed framework was only subjected to one case study of the Miyun Reservoir and its surrounding area, thus lacking data management analysis at a larger spatial scale. Future work may attempt to manage larger volumes of satellite imagery data (including regional, provincial, and national levels). Importantly, parallel computing and distributed technologies will be introduced to improve the efficiency of processing large volumes of satellite image data, taking into account the GEE (Google Earth Engine)'s strategy. Thirdly, the proposed framework only addressed the monitoring study of vegetation and water body changes in the study area, thus lacking experimental analysis for more application scenarios. Future work may extend the proposed framework to other applications to further validate its usability. Fourthly, the current research of the ODC grid is still limited within a few countries and regions and lacks cooperation among different ODC research teams, which is a pressing problem to be solved worldwide. Therefore, future work may consider increasing collaborations with other ODC research teams and sharing code and experience in organizing and managing Chinese imagery data using the ODC grid on platforms such as GitHub and Slack. Lastly, in future work, we will optimize the existing data preprocessing algorithms and performance comparative analysis dimensions to make the experimental results more rigorous and accurate. Compared with existing geospatial cloud computing platforms, such as Sentinel Hub, Google Earth Engine (GEE), or Microsoft Azure, the advantages of the grid framework in the paper are as follows: the core code framework of ODC grids is open-source, whereby any team or individual can download the code and customize development and use according to specific data management and application needs. Meanwhile, the disadvantage of ODC grids is that the data interoperability of ODC grids needs to be further improved. The OGC (Open Geospatial Consortium) community is also discussing the development of related protocols to standardize the data interoperability of ODC grids. Conceptualization, Q.C., G.L.