10.1594/PANGAEA.973635Guo, JingJingGuo0009-0005-5628-4721Beijing Normal UniversityJiao, ZitiZitiJiaoBeijing Normal UniversityPANGAEA2024Arcticmultiangular databasePOLDERsnow BRDFterrain effectionvegetation and mountain complexDataset10.1594/PANGAEA.86409010.5194/essd-9-31-201710.1016/j.rse.2018.11.00111.9 MBytesapplication/zipCreative Commons Attribution 4.0 InternationalThe POLDER multi-angle dataset stands as one of the most significant datasets in multi-angle quantitative remote sensing, with previous applications primarily focusing on non-snow-covered surfaces. This dataset provides a more detailed classification of POLDER snow Bidirectional Reflectance Distribution Function (BRDF) data located within the Arctic Circle in 2008 based on the previous database (Breon and Maignan, 2017). Initially, constraints were applied to the angular sampling distribution, retaining pixels only with observations present in both forward and backward scattering directions on the principal plane. Subsequently, the high-precision snow BRDF model RossThick-LiSparse Reciprocal Snow model (RTLSRS) (Jiao et.al, 2019) was employed to constrain the data uncertainty, removing pixels with RMSE greater than 0.04 in red band. After screening, a total of 153 pixels were retained, followed by further detailed classification, validation using corresponding MOD10A2 products, supplemented by ArcticDEM and vegetation distribution maps within the Arctic Circle. Ultimately, these datasets were categorized into snow (shady slope, sunny slope) and non-snow (vegetation-dominated, mountain complex-dominated) categories. Such datasets hold significant importance for future applications of multi-angle data on snow-covered surfaces and represent further analysis and optimization of existing snow databases.0.090.0National Natural Science Foundation of Chinahttps://doi.org/10.13039/50110000180941971288Research on Remote Sensing Inversion of Vegetation Clumping Index, Scale Effects, and Product Validation MethodsNational Natural Science Foundation of Chinahttps://doi.org/10.13039/50110000180942090013Theoretical and Methodological Research on Land Surface Intelligent Quantitative Remote Sensing / Active-Passive Synergistic Remote Sensing Modeling and Intelligent Inversion of Carbon Cycle Vegetation Structural Parameters / Multi-Source Data Information Assessment and Intelligent Inversion of Vegetation Parameters