Intelligently sharing and reusing the knowledge developed by application practices are the keys to break through technical barriers and fully activate and release the effectiveness of Earth Observations(EO).The Earth Observation Knowledge Hub(EOKH),which is used to couple decentralized knowledge bases organically,is a research frontier for the governance and intelligent service of global EO applications.In the design of Group on Earth Observations(GEO),the GEO Knowledge Hub(GKH)is intended to provide authoritative,validated,and reproducible content for evidence-based reporting on policy commitments and decision-making.Thus,the GKH offers a platform for users to discover,learn about,and employ methods,analytical tools,and applications;it also provides opportunities for the GEO community to collaborate and provide mutual assistance related to GKH contents.However,important lessons,such as the sensitive issue of intellectual ethics and how to profit GKH from the recent technological advances in information technologies,have been learned during the implementations.In response to the problems and insights encountered by GEO in developing GKH,we systematically analyzed the connotation and characteristics of EOKH and sorted out the fundamental needs and challenges for the development of EOKH in China. First,we systematically analyzed the connotation and characteristics of EOKH.The study argues that EOKH is the intersection node of high-throughput trusted EO knowledge in knowledge-sharing networks.It has three typical features,i.e.,connectivity,high throughput,and lightweight computing.Its core mission is to identify and transfer valuable research in a timely manner and to promote high throughput of application packages.Second,we sorted out the fundamental needs and challenges for the development of EOKH in China.Considering the latest progress in the study of EO ontology,we also analyzed the possible key technical problems and gave strategies to cope with them.On this basis,the system architecture prototype of EOKH,which is drawn on the system design concept of representational state transfer,is proposed,and an ontology model of conceptual EO knowledge and a formalization model of process-oriented EO knowledge are established. The study argues that EOKH should be in an open collaborative environment where humans are in the loop.The key technologies are system metrics,knowledge transfer,knowledge reuse,and knowledge exploration and visualization.The transfer and reuse of knowledge packages can greatly enhance the ease of development and reuse of EO application practices.Ontology modeling helps formalize the intrinsic connection between the human-cyber-physical systems of EO application and enhances the interpretability of higher-order complex problems.EOKH transforms knowledge sharing from point to point at the element level to group collaborative at the system level,which not only reduces the cost of cross-industry and socially integrated EO applications but also avoids repetitive research inputs,helps break through technical barriers and cognitive obstacles,and releases the effectiveness of satellite digital economy services comprehensively.The study argues that connecting EO knowledge in the human-cyber-physical systems and exploring high-throughput knowledge coproduction and transfer technology in a"humans-in-the-loop"environment are necessary to enhance the interpretability of tacit EO knowledge and promote the competitiveness and activeness of EOKH.

P. ginseng is a precious traditional Chinese functional food, which is used for both medicinal and food purposes, and has various effects such as immunomodulation, anti-tumor and anti-oxidation. The growth year of P. ginseng has an important impact on its medicinal and economic values. Fast and nondestructive identification of the growth year of P. ginseng is crucial for its quality evaluation. In this paper, we propose a FC-CNN network that incorporates spectral and spatial features of hyperspectral images to characterize P. ginseng from different growth years. The importance ranking of the spectra was obtained using the random forest method for optimal band selection. Based on the hyperspectral reflectance data of P. ginseng after radiometric calibration and the images of the best five VNIR bands and five SWIR bands selected, the year-by-year identification of P. ginseng age and its identification experiments for food and medicinal purposes were conducted, and the FC-CNN network and its FCNN and CNN branch networks were tested and compared in terms of their effectiveness in the identification of P. ginseng growth years. It has been experimentally verified that the best year-by-year recognition was achieved by utilizing images from five visible and near-infrared important bands and all spectral curves, and the recognition accuracy of food and medicinal use reached 100%. The FC-CNN network is significantly better than its branching model in the effect of edible and medicinal identification. The results show that for P. ginseng growth year identification, VNIR images have much more useful information than SWIR images. Meanwhile, the FC-CNN network utilizing the spectral and spatial features of hyperspectral images is an effective method for the identification of P. ginseng growth year.

The Advanced Geostationary Radiation Imager (AGRI) is one of the main imaging sensors on the Fengyun-4A (FY-4A) satellite. Due to the combination of high spatial and temporal resolution, the AGRI is suitable for continuously monitoring atmospheric aerosol. Existing studies only perform AOD retrieval on the dark target area of FY-4A/AGRI, and the full disk AOD retrieval is still under exploration. The Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) based on the Fully Connected Neural Network (FCNN) was used to retrieve FY-4A/AGRI full disk aerosol optical depth (AOD). The data from 111 ground-based Aerosol Robotic Network (AERONET) and Sun–Sky Radiometer Observation Network (SONET) sites were used to train the neural network, and the data from 28 other sites were used for independent validation. FY-4A/AGRI AOD data from 2017 to 2020 were validated over the full disk and three different surface types (vegetated areas, arid areas, and marine and coastal areas). For general validation, the AOD predicted by the application of NNAeroG to FY-4A/AGRI observations is consistent with the ground-based reference AOD data. The validation of the FY-4A/AGRI AOD versus the reference data set shows that the root-mean-square error (RMSE), mean absolute error (MAE), R squared (R2), and percentage of data with errors within the expected error ± (0.05 + 15%) (EE15) are 0.237, 0.145, 0.733, and 58.7%, respectively. The AOD retrieval accuracy over vegetated areas is high but there is potential for improvement of the results over arid areas and marine and coastal areas. AOD retrieval results of FY-4A/AGRI were compared under fine and coarse modes. The retrieved AOD has low accuracy in coarse mode but is better in coarse–fine mixed mode and fine mode. The current AOD products over the ocean of NNAeroG-FY4A/AGRI are not recommended. Further development of algorithms for marine areas is expected to improve the full disk AOD retrieval accuracy.

Cloud cover is an important factor limiting the earth observation efficiency of optical imaging satellites. Existing solutions include avoiding cloudy observation time windows by onboard cloud detectors and ground monitors, which are difficult to improve satellite observation efficiency in time. In order to solve the problem, firstly, a Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites cooperation scheme by using cloud cover information provided by GEO meteorological satellite to guide the imaging of LEO optical satellites is proposed, and the operation flow and key elements in this scheme are analyzed. Secondly, Fengyun-4 GEO meteorological satellite and its cloud mask (CLM) products are analyzed. Thirdly, an autonomous mission planning algorithm based on real-time cloud cover information is proposed. Computational results have demonstrated the effectiveness of the proposed GEO–LEO satellites cooperation scheme by taking the actual orbit and payload data of Fengyun-4 and Gaofen-1/2 satellites as examples.

Chang’E-3 is China’s first soft landing mission on an extraterrestrial celestial body. The laser Three-Dimensional Imaging (TDI) sensor is one of the key payloads of the Chang’E-3 lander. Its main task is to provide accurate 3D lunar surface information of the target landing area in real time for the selection of safe landing sites. Here, a simplified positioning model was constructed, to meet the accuracy and processing timeline requirements of the TDI sensor of Chang’E-3. By analyzing the influence of TDI intrinsic parameters, a permanent outdoor calibration field based on flat plates was specially designed and constructed, and a robust solution of the geometric calibration adjustment was realized by introducing virtual observation equations for unknowns. The geometric calibration and its absolute and relative positioning accuracy verification were carried out using multi-measurement and multi-angle imaging data. The results show that the error of TDI intrinsic parameters will produce a false obstacle with a maximum height of about 1.4 m on the plane, which will cause the obstacle avoidance system of Chang’E-3 to fail to find a suitable landing area or find a false flat area. Furthermore, the intrinsic parameters of the TDI have good stability and the accuracy of the reconstructed three-dimensional surface can reach about 4 cm after error calibration, which provides a reliable terrain guarantee for the autonomous obstacle avoidance of the Chang’E-3 lander.

Cloud occlusion is an important factor affecting flight safety and scientific observation. The calculation of Cloud Occlusion Probability (COP) is significant for the planning of the flight time and route of aircraft. Based on Himawari-8 and CloudSat satellite data, we propose a method to calculate the COP. The COP statistics were carried out on different distances in 12 directions 6 km above Beijing Capital International Airport (BCIA), at different heights and directions in the Haiyang aerostat production base, and at different times and seasons in Mount Qomolangma. It was found that the COP going in the southern direction from BCIA was greater than that in the northern direction by 0.67–3.12%, which is consistent with the climate conditions of Beijing. In Haiyang, the COP for several seasons in the direction of land was higher than in the direction of the ocean. The maximum COP for the 6 km altitude is 29.63% (summer) and the minimum COP is 7.59% (winter). The aerostat flight test can be conducted in the morning of winter and the direction of the ocean. The best scientific observation time for Mount Qomolangma is between 02:00 and 05:00 UTC in spring. With the increase in altitude, the COP gradually decreases. The research in this paper provides essential support for flight planning.