Near-Earth asteroid (NEA) impact is a serious threat faced by the international community. As a non-traditional disaster from outer space, it may bring an unprecedented risk that could threaten the security of the entire human civilization. Detecting and identifying risks through monitoring and warning is a prerequisite for implementing planetary defense activities to mitigate asteroid impact risks. After years of continuous investment and operation of NEA monitoring and warning systems, including ground-based telescopes and space-based telescopes, over 30000 NEAs have been discovered and cataloged to date, and the risks of potential NEA impacts are analyzed via dedicated systems. However, according to the theoretic model of NEA population, numerous NEAs are yet to be discovered, which may threaten the safety of people's lives and property, homeland security, and even the Earth's environment, and may lead to the global extinction of species, including humans. There was no warning information regarding the asteroid impact event in the Chelyabinsk region of Russia in 2013 before its occurrence, highlighting the blind areas in the existing NEA monitoring and warning systems. To ensure the long-term sustainable development of humankind, this paper focuses on the overall need for "comprehensive monitoring" and "accurate early warning" of NEAs, analyzes the current status of NEA monitoring and warning, and proposes a "six-degree" analysis framework to evaluate the capability of NEA monitoring and warning systems. The implications of "six-degree", namely "depth", "width", "frequency", "precision", "dimension", and "speed", are discussed theoretically. This study provides basic theoretical support for promoting the planning, demonstration, design, construction, and operation of NEA monitoring and warning systems, thereby helping build a community of shared future for humankind.

GF-1—GF-7 satellite series with 19 major payloads has been launched with the continuous implementation of the highresolution Earth Observation System（referred to as GF） in the past decade. This progress is vital in forming the multispectral and multimode observation capability of China’s Earth Observation System. Remote sensing data with high spatial, temporal, and spectral resolution have been obtained and widely used in scientific research and remote sensing applications. However, obtaining high-quality remote sensing information products from the original satellite data is a complicated scientific issue and faces huge challenges. Hence, the conversion chain from GF data to information must be urgently set up to reduce the remote sensing application threshold and improve the effectiveness of application services.The errors of remote sensing quantitative products are determined by accumulating a series of errors, such as sensor imaging error,calibration error, remote sensing data processing error, and quantitative inversion error. Thus, improving the accuracy of quantitative remote sensing products is a complex system engineering. Completing the whole process, including data processing, retrieval algorithm development, product generation, and validation independently, is challenging. Remote sensing algorithm test and product validation are the two crucial ways for the quality improvement of remote sensing products. Hence, this study proposes the technique system of GF common product generation and validation to improve the quality of GF remote sensing products further, thereby guaranteeing the improvement of the application quality and the extensive application area of GF remote sensing products. Lastly, the current progress of the GF common product validation and algorithm determination system platform is introduced and discussed.GF common products are required by more than two thematic remote sensing products. They can be validated using in situ observations.According to the GF common product system, the number of 39 + 6 products in seven categories are sorted out for the common requirements of multiple users, including geometric products, basic radiation products, land cover and land type products, energy balance products, vegetation products, water products, and atmosphere products. This study presents the technique flowchart of GF common product algorithm determination and product generation. The key technologies of algorithm testing, algorithm optimization, product generation, and validation are developed. Eleven national standards for remote sensing product validation are issued and implemented. Other group standards, such as GF common product generation, ground in situ observation, and validation of GF common remote sensing products, are being designed and compiled. Based on these validation technologies and the in situ data from the national network of GF remote sensing product validation field sites, the GF common product validation platform and product algorithm determination system platform can ensure the high quality of GF common products.Building such a technical system for GF common product generation and validation has great relevance for ensuring high accuracy and high quality to improve the efficiency of application services further. It requires the cooperation of multiple researchers from different units to research and develop common product retrieval algorithms. Moreover, the algorithm should be continuously tested to improve the accuracy of common products.