Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2021
Research article |
| 18 Feb 2021
| 18 Feb 2021
Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
Xiongxin Xiao
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Shunlin Liang
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742, USA
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Daiqiang Wu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Congyuan Pei
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Jianya Gong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
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Cited
21 citations as recorded by crossref.
- 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
- Spatial Downscaling of Snow Water Equivalent Using Machine Learning Methods Over the Zayandehroud River Basin, Iran M. Moradizadeh et al. 10.1007/s41064-023-00249-9
- Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods Y. Yan et al. 10.1109/JSTARS.2025.3562216
- HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model Y. Huang et al. 10.5194/essd-14-4445-2022
- A Snow Depth Downscaling Algorithm Based on Deep Learning Fusion of Enhanced Passive Microwave and Cloud-Free Optical Remote Sensing Data in China Z. Zhao et al. 10.3390/rs16244756
- Assessment of snow cover mapping algorithms from Landsat surface reflectance data and application to automated snowline delineation X. Xiao & S. Liang 10.1016/j.rse.2024.114163
- Significant decreasing trends in snow cover and duration in Northeast China during the past 40 years from 1980 to 2020 Y. Wei et al. 10.1016/j.jhydrol.2023.130318
- Estimating fractional snow cover in vegetated environments using MODIS surface reflectance data X. Xiao et al. 10.1016/j.jag.2022.103030
- Snow depth retrieval from microwave remote sensing by combining wavelet transform and machine learning models in Northern Xinjiang, China H. Hou et al. 10.1117/1.JRS.18.024517
- A novel fine-resolution snow depth retrieval model to reveal detailed spatiotemporal patterns of snow cover in Northeast China Y. Wei et al. 10.1080/17538947.2023.2196446
- Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. 10.1080/17538947.2023.2299208
- Improving Fractional Snow Cover Retrieval From Passive Microwave Data Using a Radiative Transfer Model and Machine Learning Method X. Xiao et al. 10.1109/TGRS.2021.3128524
- Estimation of fine spatial resolution all-sky surface net shortwave radiation over mountainous terrain from Landsat 8 and Sentinel-2 data Y. Ma et al. 10.1016/j.rse.2022.113364
- Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain P. Torralbo et al. 10.3390/rs15225365
- Detection of Surface Crevasses over Antarctic Ice Shelves Using SAR Imagery and Deep Learning Method J. Zhao et al. 10.3390/rs14030487
- Suitability analysis of human activities over Antarctic ice shelves: an integrated assessment of natural conditions based on machine learning algorithms B. Yang et al. 10.1080/17538947.2023.2283490
- Towards a gapless 1 km fractional snow cover via a data fusion framework X. Xiao et al. 10.1016/j.isprsjprs.2024.07.018
- Heterogeneous changes of soil microclimate in high mountains and glacier forelands S. Marta et al. 10.1038/s41467-023-41063-6
- Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data A. Jia et al. 10.1016/j.isprsjprs.2023.01.004
- Performance of climate reanalyses in the determination of pan-Arctic terrestrial rain-on-snow events J. Tao et al. 10.1016/j.accre.2023.08.002
- Effects of climate change on river-ice processes and ice jams B. Burrell et al. 10.1080/15715124.2021.2007936
20 citations as recorded by crossref.
- 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
- Spatial Downscaling of Snow Water Equivalent Using Machine Learning Methods Over the Zayandehroud River Basin, Iran M. Moradizadeh et al. 10.1007/s41064-023-00249-9
- Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods Y. Yan et al. 10.1109/JSTARS.2025.3562216
- HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model Y. Huang et al. 10.5194/essd-14-4445-2022
- A Snow Depth Downscaling Algorithm Based on Deep Learning Fusion of Enhanced Passive Microwave and Cloud-Free Optical Remote Sensing Data in China Z. Zhao et al. 10.3390/rs16244756
- Assessment of snow cover mapping algorithms from Landsat surface reflectance data and application to automated snowline delineation X. Xiao & S. Liang 10.1016/j.rse.2024.114163
- Significant decreasing trends in snow cover and duration in Northeast China during the past 40 years from 1980 to 2020 Y. Wei et al. 10.1016/j.jhydrol.2023.130318
- Estimating fractional snow cover in vegetated environments using MODIS surface reflectance data X. Xiao et al. 10.1016/j.jag.2022.103030
- Snow depth retrieval from microwave remote sensing by combining wavelet transform and machine learning models in Northern Xinjiang, China H. Hou et al. 10.1117/1.JRS.18.024517
- A novel fine-resolution snow depth retrieval model to reveal detailed spatiotemporal patterns of snow cover in Northeast China Y. Wei et al. 10.1080/17538947.2023.2196446
- Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. 10.1080/17538947.2023.2299208
- Improving Fractional Snow Cover Retrieval From Passive Microwave Data Using a Radiative Transfer Model and Machine Learning Method X. Xiao et al. 10.1109/TGRS.2021.3128524
- Estimation of fine spatial resolution all-sky surface net shortwave radiation over mountainous terrain from Landsat 8 and Sentinel-2 data Y. Ma et al. 10.1016/j.rse.2022.113364
- Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain P. Torralbo et al. 10.3390/rs15225365
- Detection of Surface Crevasses over Antarctic Ice Shelves Using SAR Imagery and Deep Learning Method J. Zhao et al. 10.3390/rs14030487
- Suitability analysis of human activities over Antarctic ice shelves: an integrated assessment of natural conditions based on machine learning algorithms B. Yang et al. 10.1080/17538947.2023.2283490
- Towards a gapless 1 km fractional snow cover via a data fusion framework X. Xiao et al. 10.1016/j.isprsjprs.2024.07.018
- Heterogeneous changes of soil microclimate in high mountains and glacier forelands S. Marta et al. 10.1038/s41467-023-41063-6
- Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data A. Jia et al. 10.1016/j.isprsjprs.2023.01.004
- Performance of climate reanalyses in the determination of pan-Arctic terrestrial rain-on-snow events J. Tao et al. 10.1016/j.accre.2023.08.002
1 citations as recorded by crossref.
Latest update: 18 May 2025
Short summary
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for...
