Preprints
https://doi.org/10.5194/essd-2021-395
https://doi.org/10.5194/essd-2021-395
 
15 Feb 2022
15 Feb 2022
Status: a revised version of this preprint is currently under review for the journal ESSD.

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

Yinghong Jing1, Xinghua Li2, and Huanfeng Shen1,3 Yinghong Jing et al.
  • 1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
  • 2School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 3Collaborative Innovation Centre of Geospatial Technology, Wuhan 430079, China

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).

Yinghong Jing et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-395', Anonymous Referee #1, 16 Mar 2022
    • AC1: 'Reply on RC1', Xinghua Li, 22 Mar 2022
  • RC2: 'Comment on essd-2021-395', Anonymous Referee #2, 25 Mar 2022
    • AC2: 'Reply on RC2', Xinghua Li, 01 Apr 2022
  • RC3: 'Comment on essd-2021-395', Anonymous Referee #3, 08 May 2022
    • AC3: 'Reply on RC3', Xinghua Li, 15 May 2022

Yinghong Jing et al.

Data sets

STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China Yinghong Jing, Xinghua Li, and Huanfeng Shen https://doi.org/10.5281/zenodo.5644386

Yinghong Jing et al.

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Short summary
Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection for China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.