STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China

Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. areas with notably diverse characteristics extensively distributed China, mainly including Northern Xinjiang (NX), Northeast China (NC), the Qinghai–Tibet Plateau (QTP). Spatiotemporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, formidable challenge of cloud obscuration severely impedes data collection. In past decades, abundant binary cover area (SCA) maps have been retrieved from moderate resolution imaging spectroradiometer (MODIS) datasets. However, integrated normalized difference index (NDSI) containing additional details on extent still extremely scarce. this study, a recent 20-year stretch seamless Terra–Aqua MODIS NDSI collection generated using Spatio-Temporal Adaptive fusion method erroR correction (STAR), which comprehensively considers spatial temporal contextual information. Evaluation tests confirm that cloud-free STAR superior to two baseline The omission error decreased by 10 % NX compared product, average correlation coefficient increased 0.11 global cloud-gap-filled product. Consequently, can serve as basic dataset climatic modeling explore various critical environmental issues China. This available https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).