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.
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.
the Creative Commons Attribution 4.0 License.
STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China
Yinghong Jing
School of Resource and Environmental Sciences, Wuhan University, Wuhan
430079, China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Huanfeng Shen
CORRESPONDING AUTHOR
School of Resource and Environmental Sciences, Wuhan University, Wuhan
430079, China
Collaborative Innovation Centre of Geospatial Technology, Wuhan
430079, China
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Cited
15 citations as recorded by crossref.
- An Improved Spatiotemporal Fusion Algorithm for Monitoring Daily Snow Cover Changes With High Spatial Resolution Y. Wang et al. 10.1109/TGRS.2022.3224126
- Towards a gapless 1 km fractional snow cover via a data fusion framework X. Xiao et al. 10.1016/j.isprsjprs.2024.07.018
- Reconstructing MODIS normalized difference snow index product on Greenland ice sheet using spatiotemporal extreme gradient boosting model F. Ye et al. 10.1016/j.jhydrol.2024.132277
- Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia G. Deng et al. 10.3390/rs16010192
- An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales L. Keuris et al. 10.3390/rs15051231
- Development of a cloud-free MODIS NDSI dataset (2001–2020) over Northeast China H. Guo et al. 10.1080/17538947.2024.2398062
- BI or IB: Which Better Generates High Spatiotemporal Resolution NDSI by Fusing Sentinel-2A/B and MODIS Data? L. Dong et al. 10.1109/JSTARS.2023.3347202
- A Novel Approach for Cloud-Free MODIS NDSI Reconstruction on the Tibetan Plateau Combining Spatiotemporal Cube and Environmental Features L. Dong et al. 10.1109/TGRS.2025.3542095
- A mechanism-guided machine learning method for mapping gapless land surface temperature J. Ma et al. 10.1016/j.rse.2024.114001
- Machine learning-based estimation of fractional snow cover in the Hindukush Mountains using MODIS and Landsat data A. Haseeb Azizi et al. 10.1016/j.jhydrol.2024.131579
- Snow Cover Phenology Change and Response to Climate in China during 2000–2020 Q. Zhao et al. 10.3390/rs14163936
- Rolling forecast of snowmelt floods in data-scarce mountainous regions using weather forecast products to drive distributed energy balance hydrological model G. Zhou et al. 10.1016/j.jhydrol.2024.131384
- A Two-Step Framework for Generating 0.01°, Hourly, and Gapless Land Surface Temperature J. Ma et al. 10.1109/JSTARS.2024.3503578
- Süleymaniye (Gümüşhane) Kış Turizm Merkezi İçin Bir Uygunluk Analizi Araştırması H. BAĞCI et al. 10.18795/gumusmaviatlas.1123825
- STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China Y. Jing et al. 10.5194/essd-14-3137-2022
13 citations as recorded by crossref.
- An Improved Spatiotemporal Fusion Algorithm for Monitoring Daily Snow Cover Changes With High Spatial Resolution Y. Wang et al. 10.1109/TGRS.2022.3224126
- Towards a gapless 1 km fractional snow cover via a data fusion framework X. Xiao et al. 10.1016/j.isprsjprs.2024.07.018
- Reconstructing MODIS normalized difference snow index product on Greenland ice sheet using spatiotemporal extreme gradient boosting model F. Ye et al. 10.1016/j.jhydrol.2024.132277
- Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia G. Deng et al. 10.3390/rs16010192
- An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales L. Keuris et al. 10.3390/rs15051231
- Development of a cloud-free MODIS NDSI dataset (2001–2020) over Northeast China H. Guo et al. 10.1080/17538947.2024.2398062
- BI or IB: Which Better Generates High Spatiotemporal Resolution NDSI by Fusing Sentinel-2A/B and MODIS Data? L. Dong et al. 10.1109/JSTARS.2023.3347202
- A Novel Approach for Cloud-Free MODIS NDSI Reconstruction on the Tibetan Plateau Combining Spatiotemporal Cube and Environmental Features L. Dong et al. 10.1109/TGRS.2025.3542095
- A mechanism-guided machine learning method for mapping gapless land surface temperature J. Ma et al. 10.1016/j.rse.2024.114001
- Machine learning-based estimation of fractional snow cover in the Hindukush Mountains using MODIS and Landsat data A. Haseeb Azizi et al. 10.1016/j.jhydrol.2024.131579
- Snow Cover Phenology Change and Response to Climate in China during 2000–2020 Q. Zhao et al. 10.3390/rs14163936
- Rolling forecast of snowmelt floods in data-scarce mountainous regions using weather forecast products to drive distributed energy balance hydrological model G. Zhou et al. 10.1016/j.jhydrol.2024.131384
- A Two-Step Framework for Generating 0.01°, Hourly, and Gapless Land Surface Temperature J. Ma et al. 10.1109/JSTARS.2024.3503578
Latest update: 01 Apr 2025
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.
Snow variation is a vital factor in global climate change. Satellite-based approaches are...
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