Analysis Ready Data (ARD) has been greatly recommended by the Committee on Earth Observation Satellites (CEOS) for simplifying and fostering long time series analysis at large scale with minimum additional user effort. Landsat ARD has been successfully made and widely used for large scale analysis. Subsequently, the Chinese satellite data similar to Landsat data have been processed and will be processed into ARDs to promote the use of the Chinese satellite data. At the first stage of the mission, the 4 Wide Field Viewing (WFV) data on GaoFen 1 (GF1) covering the whole of China and the surrounding areas have been processed into ARD. The ARD is provided as standard tiles under a common and unified projection with per pixel quality assurance and metadata for tracing back and further processing data, which are finally stored into a Hierarchical Data File (HDF); furthermore, all spectral bands are georegistered and radiometrically cross-calibrated as top of atmosphere (TOA) reflectance and are atmospherically corrected as surface reflectance (SR). Therefore, the ARD can be further used easily to produce land cover and land cover change maps and retrieve geophysical and biophysical parameters.

With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.

The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved.