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 (NX), Northeast China (NC), and the Qinghai–Tibet Plateau (QTP). Spatiotemporal 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 Terra–Aqua MODIS NDSI collection in China is generated using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which comprehensively considers spatial and temporal contextual information. Evaluation tests confirm that the cloud-free STAR NDSI collection is superior to the two baseline datasets. The omission error decreased by 10 % in NX compared to the snow cover extent product, and the average correlation coefficient increased by 0.11 compared to the global cloud-gap-filled MODIS NDSI product. Consequently, this collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues in China. This collection is available from https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

“…The global cloud-gap-filled MODIS NDSI dataset (MOD10A1F) were generated by retaining clear-sky views of the surface from previous days in MOD10A1 NDSI product to fill the cloud-covered pixel [81,82]. However, this product performs poorly in China, where periodic and transient snow is dominant [59], and so does HMA. Jing et al [59] developed the Spatio-Temporal Adaptive fusion method with erroR correction to generate the cloud-free STAR NDSI collection.…”

“…In addition, since the NDSI from C6 is a more accurate description index of the snow detection as compared to the FSC from C5 [56], more studies are shifting to the use of MODIS C6 products. Research works have demonstrated that the MODIS snow cover product C6 has high accuracy in the Tibetan Plateau, with Terra product C6 being comparable to Terra product C5, and Aqua product C6 truly having better accuracy than Aqua product C5 [57][58][59]. However, the data gaps from cloud contamination in C6 still exist, and thus a cloud removal algorithm for MODIS NDSI products is necessary.…”

Broadleaf tree phenology and springtime wildfire occurrence in boreal Canada

“…The actual date of snow melt was identified on a pixelwise, per‐annum basis as the first three consecutive days with NDSI below 0.35, corresponding to 35% snow cover averaged across the pixel. This threshold was determined by a sensitivity analysis; including thresholds of 10%, 15%, 35%, and 50%, based on the thresholds of 50% and 10% as used by O'Leary et al (2018) and Jing et al (2022), respectively. We concluded that 35% gave snow melt dates that aligned with those of O'Leary et al (2018) and of ERA‐5 Land snow cover data, whereas lower thresholds were unduly late, likely influenced by surface water, and higher thresholds were characterized by a high degree of noise and erroneous mid‐winter snow melt.…”

BI or IB: Which Better Generates High Spatiotemporal Resolution NDSI by Fusing Sentinel-2A/B and MODIS Data?

“…However, this product has data gaps caused by cloud cover. Shen et al utilized the spatio-temporal adaptive fusion method with error correction (STAR) to fill the data gaps, and obtained a daily stretched seamless NDSI product for China with 500 m spatial resolution [37]. Still, for the TP, which is characterized by complex topography and strong spatial heterogeneity snow cover, it is difficult to obtain detailed and accurate snow cover distribution or change information from such data, so the spatial and temporal resolution of optical remote sensing data is highly demanded.…”