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

Abstract

Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. Snow areas with notably diverse characteristics are extensively distributed in China, mainly including Northern Xinjiang (XJ), Northeast China (NC), and Tibetan Plateau (TP). Spatio-temporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, the formidable challenge of cloud obscuration severely impedes data collection. In the past decades, abundant binary snow cover area (SCA) maps have been retrieved from moderate resolution imaging spectroradiometer (MODIS) datasets. However, the integrated normalized difference snow index (NDSI) maps containing additional details on snow cover extent are still extremely scarce. In this study, a recent 20-year stretch seamless MODIS NDSI collection in China is generated for the first time using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which comprehensively considers spatial and temporal contextual information. Evaluation tests confirm that the gap-filled STAR NDSI collection is highly consistent with the Landsat NDSI dataset, with an average correlation coefficient of approximately 0.84. Consequently, this collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues. This collection is available from https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).