遥感产品是地球资源监测的重要数据来源，随着产品生产与服务体系不断完善，推进高质量遥感产品发展迫在眉睫。遥感产品质量问题是多源误差综合影响的结果，虽然真实性检验方法给出了产品的整体精度，但无法反映各环节误差的具体来源及大小。为了保证产品的可靠性，地球质量保障框架（QA4EO）提供的指导文件中特别强调了遥感产品误差溯源，旨在将遥感产品的误差追溯到统一的参考基准或国际标准。本文聚焦于遥感产品生产与检验中的误差溯源，分析了测量与遥感产品的误差来源，阐述了真实性检验与误差溯源的基本内涵，总结了遥感产品生产与检验中误差溯源的基本方法及其典型应用，并对误差溯源的未来发展趋势进行展望。论文的研究成果可为进一步实现遥感产品生产与真实性检验提供理论和技术支持。

Remote sensing products are of great significance for Earth resource monitoring, environmental governance and climate change research. With the continuous enhancement of the product production and service system, it is highly urgent to facilitate the development of high-quality remote sensing products. The quality of remote sensing products is inherently influenced by multi-source errors originating from sensor performance limitations, radiometric calibration, atmospheric correction, geometric distortions and retrieval uncertainties. Errors introduced at each stage are cumulatively propagated, leading to significant uncertainty in the quality of the derived remote sensing products. Although the validation method offers quantitative assessment, it cannot reflect the specific sources and magnitudes of errors in detail. To ensure the reliability and consistency of these products, the guidance documents provided by the Quality Assurance Framework for Earth Observation (QA4EO) particularly emphasize that it is crucial for data and derived products to be associated with an indicator of quality that is traceable to reference standards (preferably the International System of Units - SI). This traceability framework not only enables quantitative inter-product comparisons but also objectively characterizes their accuracy disparities. This paper mainly focuses on error traceability in remote sensing product production and validation. Errors originate from two distinct sources: intrinsic product errors and measurement uncertainties associated with ground reference data. The paper commences with a detailed description of the origins of these errors in both components. Subsequently, it elucidates the fundamental concepts of validation and error traceability, highlighting the significance of establishing SI-traceable propagation chains. Additionally, three main methods of error traceability in remote sensing product production and validation are also summarized, including uncertainty estimation, error decomposition and combining algorithm test and validation. The core objective is to determine the sources and magnitudes of errors accurately at each stage of product production and validation. Typical applications of these methods are illustrated through case studies, which provide a foundation for analyzing error propagation mechanisms and developing error propagation models. Finally, the conclusion and prospect of the future development trend of error traceability are summarized. Understanding the law of error propagation and establishing a comprehensive error transmission chain are crucial issues to be addressed in error traceability research. It is vital for algorithm improvement and product quality enhancement. Currently, the study of error traceability in remote sensing products remains in its infancy, with relatively few existing investigations in this area. Moreover, developing traceable quality indicators system that fully reflect uncertainties arising from input data, processing, and validation procedures is also an essential component of error traceability. These indicators serve as a vital means of presenting traceability results. Both producers and users are concerned about the quality of remote sensing products. The findings of this study provide theoretical and technological support for the further advancement of remote sensing product production and validation. However, how to further address the key issues of error traceability in the future, particularly in terms of constructing a whole-process error propagation model and developing a traceability method that considers both accuracy and uncertainty, remains a critical area that requires in-depth investigation.