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

Jing, Yinghong; Li, Xinghua; Shen, Huanfeng

doi:https://doi.org/10.5194/essd-14-3137-2022

Articles | Volume 14, issue 7

https://doi.org/10.5194/essd-14-3137-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-14-3137-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 7

Data description paper

|

11 Jul 2022

Data description paper |

| 11 Jul 2022

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

Yinghong Jing, Xinghua Li, and Huanfeng Shen

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

MODIS daily cloudless binary snow products in Northern Hemisphere from 2000 to 2015 X. Huang https://doi.org/10.12072/ncdc.CCI.db0044.2020

Daily Fractional Snow Cover (FSC) Data set over High Asia Y. Qiu, X. Wang, L. Han, L. Chang, and L. Shi https://doi.org/10.11922/sciencedb.457

Observational snow depth dataset of the Tibetan Plateau (Version 1.0) (1961-2013) National Meteorological Information Center and Tibet Meteorological Bureau, China https://doi.org/10.11888/Snow.tpdc.270558

MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61 D. K. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MOD10A1F.061

Model code and software

A spatio-temporal adaptive fusion method with error correction for cloud-free MODIS NDSI estimation (Version 01) Yinghong Jing https://doi.org/10.5281/zenodo.6396149

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 in 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.