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The spectral characteristics of ground objects are the theoretical basis for target detection and identification using remote sensing. The spectral library plays an important role in improving the level of remote sensing applications. In order to standardize and enrich China"s spectral database and promote its application in the fields of surveying and mapping, we have established “the Ground Object Background Spectral Library for Surveying and Mapping (GOSPEL)” based on The National Science and Technology Foundation Project. Important progress has been made in warehousing, construction of the ground feature spectrum library, and preliminary exploration of application demonstrations. Based on the existing spectrum acquisition and processing specifications, Ground Spectral Spectrum and Supporting Non-spectral Data Collection and Summary Standards, Spectral and supporting non-spectral parameter test technical specifications, and Feature Classification Code Standards have been established. Since the implementation of the project, in accordance with the data norms and standards formulated by the project, more than 14000 pieces of existing spectral data was compiled and stored. At the same time, nationwide spectrum collection experiments have been organized and implemented, involving major regions in China. A total of more than 17,000 ground feature spectra were acquired by these experiments. Full-band experiments (visible-near infrared band, infrared band and microwave band) were performed in North and Northeast China, getting full-band data of snow, soil, vegetation canopy and artificial target. In order to facilitate the application and sharing of spectral data, a ground feature spectral database (GOSPEL) and a data sharing platform containing more than 30,000 pieces of data have been constructed. According to the type of ground feature spectral data and application requirements, there are a total of 25 datasets formed, such as the full-band typical feature spectrum data set, the multi-angle spectral reflectance data set, the multi-scale typical feature reflectance data set and the long-term series spectral data set. We have performed application demonstrations on ground feature classification, remote sensing mechanism simulation, remote sensing quantitative inversion, and remote sensing product validation based on the current ground feature spectrum from GOSPEL.