Development of a 10 m daily seamless surface reflectance data cube based on Sentinel-2 constellation for generating the reference true-value products at Wanglang mountain area, China

Mountain ecosystems, characterized by high heterogeneity and rapid dynamics, require high spatiotemporal resolution remote sensing products for accurate monitoring. However, rugged terrain induces significant radiometric distortion in satellite imagery, and persistent cloud cover leads to data gaps, severely limiting the application of optical satellites in these regions. This study developed an integrated method to generate a daily, seamless, 10-m resolution, Sentinel-2-like surface reflectance data cube for the topographically complex Wanglang mountain area in China. Our method synergistically combined a physically-based topographic correction model that incorporates atmospheric and bidirectional reflectance distribution function (BRDF) effects, a harmonic model for temporal reconstruction and gap-filling, and a validation and calibration framework using a tower-based multi-angle in-situ surface reflectance observation. The results demonstrate that the physically-based topographic correction model outperformed the operational L2A product of Sentinel-2, reducing the RMSE in the near-infrared (NIR) band from 0.025–0.035 to 0.0106–0.0206 and effectively eliminating the dependence on solar incidence angle (reducing R 2 to as low as 0.007 during peak season). The harmonic model accurately reconstructed seamless daily data, achieving well prediction accuracy (R 2 of 0.83–0.87 for NIR band and 0.81–0.89 for blue band) across different phenological stages. Following linear calibration, the final data cube achieved exceptional radiometric agreement with independent in-situ measurements, with R 2 > 0.60 for all visible and NIR bands and RMSE as low as 0.0081–0.0171. This study provides not only a high-fidelity and seamless surface reflectance product in Wanglang complex terrains for the research community but also a replicable framework for generating reference true-value products in the challenging mountain area. The resulting surface reflectance data cube serves as a critical foundation for biophysical parameters estimation, thereby enhancing our ability to validate and develop mountain remote sensing algorithms and products, and further understanding vulnerable mountain ecosystems as well. • A daily 10m surface reflectance data cube is generated for a typical mountain area. • Data cube's spectral consistency was enhanced by the topographic correction model. • Data cube's spatio-temporal continuity was increased by the harmonic model. • Data cube's fidelity was validated following calibration with tower based network.