A Novel Framework for Validation of DSM Data Quality Based on Hyperspectral Topographic Correction

Digital Surface Model (DSM) products play an indispensable role in mapping production and geospatial applications. Their importance in hyperspectral topographic correction is particularly critical, as they determine key terrain parameters such as slope, aspect, and solar incidence angle. Therefore, it is essential to evaluate the quality of DSM products. To address this issue, this study proposes a novel framework to indirectly assess DSM quality from the perspective of radiometric consistency after atmospheric and topographic correction. Two DSM datasets, the Copernicus DEM (GLO-30) and the Chinese ZY-3 DSM, are integrated into a hyperspectral correction workflow using EnMAP and ZY-1 02D data over two study areas. Multiple evaluation metrics, including visual assessment, regression analysis, interquartile range (IQR), and relative difference in mean reflectance (RDMR), are employed to quantify spectral variability and radiometric stability. The results show that both Copernicus DEM and ZY-3 DSM achieve comparable performance in topographic correction, while the Copernicus DEM shows slightly better consistency across most evaluation metrics. These findings demonstrate that topographic correction outcomes inherently contain DSM quality information and that spectral consistency metrics can serve as a valuable complementary tool for DSM evaluation, particularly in the absence of ground truth data.