ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume X-1-2024
09 May 2024
 | 09 May 2024

Research on Hyperspectral Surface Reflectance Dataset of Typical Ore Concentration Area in Hami Remote Sensing Test Field

Shuneng Liang, Yang Li, Hongyan Wei, Lina Dong, Jiaheng Zhang, and Chenchao Xiao

Keywords: Hyperspectral Remote Sensing, Hami Remote Sensing Test Field, Surface Reflectance, Dataset, Ore Concentrati-on Area

Abstract. Surface reflectance data is the basic data source for the hyperspectral parametric remote sensing products and remote sensing quantitative application, which is widely used in various application fields such as natural resources and ecological environment monitoring. At present, multispectral data takes the leading role among the common land surface reflectance datasets and the reflectance data mainly involves the types of ground objects such as farmland, forest land, water body, soil, etc., while the datasets relatively less targets the types of rock and mineral surface objects, yet especially the reflectance datasets with the combination of time series and multi-scale satellite-earth are even more scarce. In order to better promote the application of hyperspectral surface reflectance and explore the advantages of joint application of satellite-earth multi-scale reflectance data, on the basis of field-measured rock and mineral target spectral, a comprehensive surface reflectance dataset was generated by using domestically produced hyperspectral satellite data as the data source in this study, mainly focusing on the typical ore concentration area in the Hami Remote Sensing test field in Xinjiang. The dataset includes multi-period hyperspectral satellite surface reflectance images, field measured rock and mineral spectral data, and multi-period sub-pixel spectral data collected based on ground spectral measured points, which can provide significant support for the research and development and accuracy verification as well as performance evaluation of algorithms such as surface reflectance inversion, mineral identification and ground object classification.